Network Meta-Analysis

Author

Caio Vallio

Published

June 30, 2026

Abstract

A network meta-analysis of fibromyalgia treatments

Code

# Install packages
# install.packages("netmeta")
# install.packages("readxl")
library(netmeta)
library(readxl)
library(dplyr)
library(tidyr)
library(kableExtra)

render_league_table <- function(nma_obj, ci = TRUE) {
  lg <- netleague(nma_obj, ci = ci)
  df <- tibble::rownames_to_column(as.data.frame(lg$random), "Comparison")
  df |>
    kable(format = "html", escape = FALSE) |>
    kable_styling(
      bootstrap_options = c("striped", "hover", "condensed"),
      full_width = TRUE,
      font_size = 12
    ) |>
    scroll_box(width = "100%")
}

render_netsplit_table <- function(split_obj) {
  split_obj$random |>
    kable(format = "html", digits = 4) |>
    kable_styling(
      bootstrap_options = c("striped", "hover", "condensed"),
      full_width = TRUE
    ) |>
    scroll_box(width = "100%")
}

netsplit_fig_height <- function(split_obj, pad = 1.03) {
  n <- nrow(split_obj$random)
  h <- if (identical(split_obj$show, "all")) {
    1.5 + n * 0.98
  } else if (n <= 40) {
    2.8 + n * 0.47
  } else {
    2.8 + 40 * 0.47 + (n - 40) * 0.15
  }
  max(4.5, h * pad)
}

plot_netsplit_forest <- function(split_obj, ...) {
  par(mar = c(2.5, 4, 1, 2), mgp = c(3, 0.6, 0))
  forest(split_obj, ...)
}

1 NMA for continuous outcomes

Note

Please: Select an outcome to proceed.

Code

# Read data
data_pim <- read_excel(
    "data/Banco de Dados_Rstudio (1).xlsx",
    sheet = "P | Im"
) |>
    mutate(
        Mean1 = as.numeric(Mean1),
        Mean2 = as.numeric(Mean2),
        SD1 = as.numeric(SD1),
        SD2 = as.numeric(SD2)
    )


# Transform to contrast-based
pw <- pairwise(
    treat = list(Treat1, Treat2),
    n = list(N1, N2),
    mean = list(Mean1, Mean2),
    sd = list(SD1, SD2),
    studlab = StudyID,
    data = data_pim
)

# Check network connections
net_con <- netconnection(pw)
net_con

Number of studies: k = 34
Number of pairwise comparisons: m = 34
Number of treatments: n = 12
Number of designs: d = 11
Number of networks: 2

Details on subnetworks: 
 subnetwork  k  m n
          1  5  5 4
          2 29 29 8

There are two sub-networks:

Subnet 1:
- 5 studies
- 5 comparisons
- 4 treatments

Subnet 2:
- 29 studies
- 29 comparisons
- 8 treatments

Tip

There are two treatment sub-networks that do not connect.

Note

Please: Select the treatment sub-networks before proceeding.

Select the procedures performed

Important

The first subnet contains 5 studies, 5 comparisons and 4 treatments.

Code

# Filter data for subnet 1
sr1 <- tibble(
    treat1 = net_con$treat1,
    treat2 = net_con$treat2,
    subnet = net_con$subnet
) |>
    filter(subnet == 1)

# Display comparison table
sr1

Code

# Unique treatments in subnet 1
unique_treat1 <- sr1$treat1 |> unique()
unique_treat2 <- sr1$treat2 |> unique()

# Combination of all unique treatments
trat_sr1 <- unique(c(unique_treat1, unique_treat2))
print(trat_sr1)

[1] "Mnt"  "Bal"  "Cry"  "WlNi"

Code

# Total number of treatments
length(trat_sr1)

[1] 4

Code

# Filter pairwise object only for comparisons within subnet 1
pw_pim_sr1 <- pw %>%
    filter(treat1 %in% trat_sr1 & treat2 %in% trat_sr1)

# Number of comparisons in subnet 1
nrow(pw_pim_sr1)

[1] 5

Code

# Fit NMA model for subnet 1
nma_sr1 <- netmeta(
    TE,
    seTE,
    treat1,
    treat2,
    studlab,
    data = pw_pim_sr1,
    n1 = n1,
    n2 = n2,
    random = TRUE,
    common = FALSE,
    sm = "MD", # can be "MD" or "SMD"
    ref = "WlNi" # Reference treatment (change if you want another control)
)

# Basic model summary
print(nma_sr1)

Number of studies: k = 5
Number of pairwise comparisons: m = 5
Number of observations: o = 232
Number of treatments: n = 4
Number of designs: d = 3

Random effects model

Treatment estimate (other treatments vs 'WlNi'):
          MD             95% CI     z  p-value
Bal  -1.2500 [-2.2645; -0.2355] -2.41   0.0157
Cry  -6.5000 [-8.0223; -4.9777] -8.37 < 0.0001
Mnt   0.0100 [-1.6642;  1.6842]  0.01   0.9907
WlNi       .                  .     .        .

Quantifying heterogeneity / inconsistency:
tau^2 = 0.4690; tau = 0.6849; I^2 = 58.5% [0.0%; 88.2%]

Tests of heterogeneity (within designs) and inconsistency (between designs):
                   Q d.f. p-value
Total           4.82    2  0.0898
Within designs  4.82    2  0.0898
Between designs 0.00    0      --

Details of network meta-analysis methods:
- Frequentist graph-theoretical approach
- DerSimonian-Laird estimator for tau^2
- Calculation of I^2 based on Q

Code

# Network plot
netgraph(
    nma_sr1,
    seq = "optimal",
    number.of.studies = TRUE,
    cex.points = n.trts,
    labels = paste0(trts, "\n(n=", round(n.trts), ")")
)

Network plot of treatments for Subnet 1 (P | Im)

Code

# Detailed model summary
summary(nma_sr1)

Original data:

            treat1 treat2      TE   seTE
Albers2018     Mnt   WlNi  0.0100 0.5105
Buskila2001    Bal   WlNi -1.7000 0.4818
Neumann2001    Bal   WlNi -0.2100 0.5836
Ozkurt2011     Bal   WlNi -1.8500 0.6816
Kiyak2022      Cry   WlNi -6.5000 0.3664

Number of treatment arms per study:
            narms
Albers2018      2
Buskila2001     2
Neumann2001     2
Ozkurt2011      2
Kiyak2022       2

Results (random effects model):

            treat1 treat2      MD             95% CI
Albers2018     Mnt   WlNi  0.0100 [-1.6642;  1.6842]
Buskila2001    Bal   WlNi -1.2500 [-2.2645; -0.2355]
Neumann2001    Bal   WlNi -1.2500 [-2.2645; -0.2355]
Ozkurt2011     Bal   WlNi -1.2500 [-2.2645; -0.2355]
Kiyak2022      Cry   WlNi -6.5000 [-8.0223; -4.9777]

Number of studies: k = 5
Number of pairwise comparisons: m = 5
Number of observations: o = 232
Number of treatments: n = 4
Number of designs: d = 3

Random effects model

Treatment estimate (other treatments vs 'WlNi'):
          MD             95% CI     z  p-value
Bal  -1.2500 [-2.2645; -0.2355] -2.41   0.0157
Cry  -6.5000 [-8.0223; -4.9777] -8.37 < 0.0001
Mnt   0.0100 [-1.6642;  1.6842]  0.01   0.9907
WlNi       .                  .     .        .

Quantifying heterogeneity / inconsistency:
tau^2 = 0.4690; tau = 0.6849; I^2 = 58.5% [0.0%; 88.2%]

Tests of heterogeneity (within designs) and inconsistency (between designs):
                   Q d.f. p-value
Total           4.82    2  0.0898
Within designs  4.82    2  0.0898
Between designs 0.00    0      --

Details of network meta-analysis methods:
- Frequentist graph-theoretical approach
- DerSimonian-Laird estimator for tau^2
- Calculation of I^2 based on Q

Code

# Forest plot
forest(nma_sr1)

Forest plot of treatment effects for Subnet 1

Code

# Pairwise comparison table with confidence intervals
render_league_table(nma_sr1)

Table 1: League table (random effects model) for Subnet 1

Comparison	Bal	Cry	Mnt	WlNi
Bal	Bal	.	.	-1.2500 [-2.2645; -0.2355]
Cry	5.2500 [ 3.4206; 7.0794]	Cry	.	-6.5000 [-8.0223; -4.9777]
Mnt	-1.2600 [-3.2176; 0.6976]	-6.5100 [-8.7728; -4.2472]	Mnt	0.0100 [-1.6642; 1.6842]
WlNi	-1.2500 [-2.2645; -0.2355]	-6.5000 [-8.0223; -4.9777]	0.0100 [-1.6642; 1.6842]	WlNi

Code

# Treatment ranking
ranking_sr1 <- netrank(nma_sr1, small.values = "good")
print(ranking_sr1)

     P-score
Cry   1.0000
Bal   0.6295
Mnt   0.1996
WlNi  0.1708

Code

# Rankogram
ran_sr1 <- rankogram(nma_sr1)
plot(ran_sr1)

Code

# Design decomposition
decomp_sr1 <- decomp.design(nma_sr1)
print(decomp_sr1)

Q statistics to assess homogeneity / consistency

                   Q df p-value
Total           4.82  2  0.0898
Within designs  4.82  2  0.0898
Between designs 0.00  0      --

Design-specific decomposition of within-designs Q statistic

   Design    Q df p-value
 WlNi:Bal 4.82  2  0.0898

Q statistic to assess consistency under the assumption of
a full design-by-treatment interaction random effects model

                   Q df p-value tau.within tau2.within
Between designs 0.00  0      --     0.6849      0.4690

Code

# Node-splitting analysis to detect inconsistency
split_sr1 <- netsplit(nma_sr1, show = "all")
render_netsplit_table(split_sr1)

Table 2: Node-splitting analysis for Subnet 1

comparison	TE	seTE	lower	upper	statistic	p
Bal:Cry	5.25	0.9334	3.4206	7.0794	5.6248	0.0000
Bal:Mnt	-1.26	0.9988	-3.2176	0.6976	-1.2615	0.2071
Bal:WlNi	-1.25	0.5176	-2.2645	-0.2355	-2.4149	0.0157
Cry:Mnt	-6.51	1.1545	-8.7728	-4.2472	-5.6387	0.0000
Cry:WlNi	-6.50	0.7767	-8.0223	-4.9777	-8.3688	0.0000
Mnt:WlNi	0.01	0.8542	-1.6642	1.6842	0.0117	0.9907

Code

# Forest plot of node-splitting
plot_netsplit_forest(split_sr1)

Figure 1: Forest plot of node-splitting for Subnet 1

Code

# Heatmap
netheat(nma_sr1)

Code

# Funnel plot
funnel(nma_sr1, order = "WlNi", )

Select the procedures performed

Important

The second subnet is more robust, containing 29 studies, 29 comparisons and 8 treatments. This is the main analysis network.

Code

# Filter data for subnet 2
sr2 <- tibble(
    treat1 = net_con$treat1,
    treat2 = net_con$treat2,
    subnet = net_con$subnet
) |>
    filter(subnet == 2)

# Display first rows of comparison table
sr2 |> head()

Code

# Unique treatments in subnet 2
unique_treat1_sr2 <- sr2$treat1 |> unique()
print(unique_treat1_sr2)

[1] "Acu"  "CBT"  "rTMS" "tDCS" "PbT"  "Elec" "MfT"

Code

unique_treat2_sr2 <- sr2$treat2 |> unique()
print(unique_treat2_sr2)

[1] "PlaSh" "tDCS"

Code

# Combination of all unique treatments
trat_sr2 <- unique(c(unique_treat1_sr2, unique_treat2_sr2))
print(trat_sr2)

[1] "Acu"   "CBT"   "rTMS"  "tDCS"  "PbT"   "Elec"  "MfT"   "PlaSh"

Code

# Total number of treatments
length(trat_sr2)

[1] 8

Code

# Filter pairwise object only for comparisons within subnet 2
pw_pim_sr2 <- pw %>%
    filter(treat1 %in% trat_sr2 & treat2 %in% trat_sr2)

# Number of comparisons in subnet 2
nrow(pw_pim_sr2)

[1] 29

Code

# Number of unique studies
length(unique(pw_pim_sr2$studlab))

[1] 29

Code

# Fit NMA model for subnet 2
nma_sr2 <- netmeta(
    TE,
    seTE,
    treat1,
    treat2,
    studlab,
    data = pw_pim_sr2,
    n1 = N1,
    n2 = N2,
    random = TRUE,
    common = FALSE,
    sm = "MD",
    ref = "PlaSh" # Reference treatment (change if you want another control)
)

# Basic model summary
print(nma_sr2)

Number of studies: k = 29
Number of pairwise comparisons: m = 29
Number of observations: o = 1120
Number of treatments: n = 8
Number of designs: d = 8

Random effects model

Treatment estimate (other treatments vs 'PlaSh'):
           MD             95% CI     z  p-value
Acu   -0.0623 [-1.4286;  1.3039] -0.09   0.9288
CBT   -1.3373 [-2.8846;  0.2099] -1.69   0.0903
Elec  -2.8500 [-4.8888; -0.8112] -2.74   0.0061
MfT   -0.4900 [-2.2935;  1.3135] -0.53   0.5944
PbT   -2.3066 [-3.5777; -1.0355] -3.56   0.0004
PlaSh       .                  .     .        .
rTMS  -1.2245 [-1.8797; -0.5692] -3.66   0.0002
tDCS  -1.1471 [-1.7012; -0.5930] -4.06 < 0.0001

Quantifying heterogeneity / inconsistency:
tau^2 = 0.5615; tau = 0.7494; I^2 = 68% [50.7%; 79.2%]

Tests of heterogeneity (within designs) and inconsistency (between designs):
                    Q d.f.  p-value
Total           68.71   22 < 0.0001
Within designs  68.09   21 < 0.0001
Between designs  0.62    1   0.4322

Details of network meta-analysis methods:
- Frequentist graph-theoretical approach
- DerSimonian-Laird estimator for tau^2
- Calculation of I^2 based on Q

Code

# Network plot
netgraph(
    nma_sr2,
    seq = "optimal",
    number.of.studies = TRUE,
    cex.points = n.trts,
    labels = paste0(trts, "\n(n=", round(n.trts), ")")
)

Network plot of treatments for Subnet 2 (P | Im)

Code

# Detailed model summary
summary(nma_sr2)

Original data:

              treat1 treat2      TE   seTE
Assefi2005       Acu  PlaSh  0.6200 0.4939
Babu2007         CBT  PlaSh -1.7000 1.0070
Boyer2014      PlaSh   rTMS -0.3000 0.7445
Brietzke2019   PlaSh   tDCS  1.7900 0.3988
Cheng2019      PlaSh   rTMS  0.9000 1.3742
Curatolo2017   PlaSh   tDCS  3.2000 0.8829
Fagerlund2015  PlaSh   tDCS  0.9600 0.5231
Fregni2006     PlaSh   tDCS -0.2800 0.8557
Gur2002a         PbT  PlaSh -2.0900 0.4526
Gur2002b         PbT  PlaSh -2.5700 0.6087
Khedr2017      PlaSh   tDCS  2.0000 0.3598
Lauretti2013    Elec  PlaSh -2.8500 0.7215
Lee2012        PlaSh   rTMS  0.5600 1.3631
Maestu2013     PlaSh   rTMS -0.4400 0.5614
Mendonça2011   PlaSh   tDCS  2.1800 1.1082
Mhalla2011     PlaSh   rTMS  1.8300 0.3834
Oka2019          MfT  PlaSh -0.4900 0.5340
Passard2007    PlaSh   rTMS  1.4400 0.5723
Short2011      PlaSh   rTMS  1.0800 0.8475
Stival2013       Acu  PlaSh -1.1000 0.8145
Tekin2014      PlaSh   rTMS  2.6300 0.4230
Valle2009      PlaSh   tDCS  1.2200 0.7126
Yagci2014      PlaSh   rTMS  1.1700 1.0040
deMelo2020     PlaSh   tDCS -0.3000 1.1882
Forogh2021      rTMS   tDCS -1.0600 0.7943
Caumo2023      PlaSh   tDCS  0.8100 0.1056
Gungomus2023     CBT  PlaSh -1.1000 0.6852
Loreti2023     PlaSh   tDCS  1.9900 0.4623
Betancur2025   PlaSh   tDCS -0.5000 0.5457

Number of treatment arms per study:
              narms
Assefi2005        2
Babu2007          2
Boyer2014         2
Brietzke2019      2
Cheng2019         2
Curatolo2017      2
Fagerlund2015     2
Fregni2006        2
Gur2002a          2
Gur2002b          2
Khedr2017         2
Lauretti2013      2
Lee2012           2
Maestu2013        2
Mendonça2011      2
Mhalla2011        2
Oka2019           2
Passard2007       2
Short2011         2
Stival2013        2
Tekin2014         2
Valle2009         2
Yagci2014         2
deMelo2020        2
Forogh2021        2
Caumo2023         2
Gungomus2023      2
Loreti2023        2
Betancur2025      2

Results (random effects model):

              treat1 treat2      MD             95% CI
Assefi2005       Acu  PlaSh -0.0623 [-1.4286;  1.3039]
Babu2007         CBT  PlaSh -1.3373 [-2.8846;  0.2099]
Boyer2014      PlaSh   rTMS  1.2245 [ 0.5692;  1.8797]
Brietzke2019   PlaSh   tDCS  1.1471 [ 0.5930;  1.7012]
Cheng2019      PlaSh   rTMS  1.2245 [ 0.5692;  1.8797]
Curatolo2017   PlaSh   tDCS  1.1471 [ 0.5930;  1.7012]
Fagerlund2015  PlaSh   tDCS  1.1471 [ 0.5930;  1.7012]
Fregni2006     PlaSh   tDCS  1.1471 [ 0.5930;  1.7012]
Gur2002a         PbT  PlaSh -2.3066 [-3.5777; -1.0355]
Gur2002b         PbT  PlaSh -2.3066 [-3.5777; -1.0355]
Khedr2017      PlaSh   tDCS  1.1471 [ 0.5930;  1.7012]
Lauretti2013    Elec  PlaSh -2.8500 [-4.8888; -0.8112]
Lee2012        PlaSh   rTMS  1.2245 [ 0.5692;  1.8797]
Maestu2013     PlaSh   rTMS  1.2245 [ 0.5692;  1.8797]
Mendonça2011   PlaSh   tDCS  1.1471 [ 0.5930;  1.7012]
Mhalla2011     PlaSh   rTMS  1.2245 [ 0.5692;  1.8797]
Oka2019          MfT  PlaSh -0.4900 [-2.2935;  1.3135]
Passard2007    PlaSh   rTMS  1.2245 [ 0.5692;  1.8797]
Short2011      PlaSh   rTMS  1.2245 [ 0.5692;  1.8797]
Stival2013       Acu  PlaSh -0.0623 [-1.4286;  1.3039]
Tekin2014      PlaSh   rTMS  1.2245 [ 0.5692;  1.8797]
Valle2009      PlaSh   tDCS  1.1471 [ 0.5930;  1.7012]
Yagci2014      PlaSh   rTMS  1.2245 [ 0.5692;  1.8797]
deMelo2020     PlaSh   tDCS  1.1471 [ 0.5930;  1.7012]
Forogh2021      rTMS   tDCS -0.0774 [-0.9015;  0.7468]
Caumo2023      PlaSh   tDCS  1.1471 [ 0.5930;  1.7012]
Gungomus2023     CBT  PlaSh -1.3373 [-2.8846;  0.2099]
Loreti2023     PlaSh   tDCS  1.1471 [ 0.5930;  1.7012]
Betancur2025   PlaSh   tDCS  1.1471 [ 0.5930;  1.7012]

Number of studies: k = 29
Number of pairwise comparisons: m = 29
Number of observations: o = 1120
Number of treatments: n = 8
Number of designs: d = 8

Random effects model

Treatment estimate (other treatments vs 'PlaSh'):
           MD             95% CI     z  p-value
Acu   -0.0623 [-1.4286;  1.3039] -0.09   0.9288
CBT   -1.3373 [-2.8846;  0.2099] -1.69   0.0903
Elec  -2.8500 [-4.8888; -0.8112] -2.74   0.0061
MfT   -0.4900 [-2.2935;  1.3135] -0.53   0.5944
PbT   -2.3066 [-3.5777; -1.0355] -3.56   0.0004
PlaSh       .                  .     .        .
rTMS  -1.2245 [-1.8797; -0.5692] -3.66   0.0002
tDCS  -1.1471 [-1.7012; -0.5930] -4.06 < 0.0001

Quantifying heterogeneity / inconsistency:
tau^2 = 0.5615; tau = 0.7494; I^2 = 68% [50.7%; 79.2%]

Tests of heterogeneity (within designs) and inconsistency (between designs):
                    Q d.f.  p-value
Total           68.71   22 < 0.0001
Within designs  68.09   21 < 0.0001
Between designs  0.62    1   0.4322

Details of network meta-analysis methods:
- Frequentist graph-theoretical approach
- DerSimonian-Laird estimator for tau^2
- Calculation of I^2 based on Q

Code

# Forest plot
forest(nma_sr2)

Forest plot of treatment effects for Subnet 2

Code

# Pairwise comparison table with confidence intervals
render_league_table(nma_sr2)

Table 3: League table (random effects model) for Subnet 2

Comparison	Acu	CBT	Elec	MfT	PbT	PlaSh	rTMS	tDCS
Acu	Acu	.	.	.	.	-0.0623 [-1.4286; 1.3039]	.	.
CBT	1.2750 [-0.7891; 3.3392]	CBT	.	.	.	-1.3373 [-2.8846; 0.2099]	.	.
Elec	2.7877 [ 0.3334; 5.2419]	1.5127 [-1.0468; 4.0721]	Elec	.	.	-2.8500 [-4.8888; -0.8112]	.	.
MfT	0.4277 [-1.8349; 2.6902]	-0.8473 [-3.2236; 1.5289]	-2.3600 [-5.0820; 0.3620]	MfT	.	-0.4900 [-2.2935; 1.3135]	.	.
PbT	2.2443 [ 0.3782; 4.1103]	0.9692 [-1.0332; 2.9717]	-0.5434 [-2.9460; 1.8591]	1.8166 [-0.3898; 4.0230]	PbT	-2.3066 [-3.5777; -1.0355]	.	.
PlaSh	-0.0623 [-1.4286; 1.3039]	-1.3373 [-2.8846; 0.2099]	-2.8500 [-4.8888; -0.8112]	-0.4900 [-2.2935; 1.3135]	-2.3066 [-3.5777; -1.0355]	PlaSh	1.1235 [ 0.4375; 1.8095]	1.2172 [ 0.6455; 1.7890]
rTMS	1.1621 [-0.3532; 2.6774]	-0.1129 [-1.7932; 1.5674]	-1.6255 [-3.7670; 0.5159]	0.7345 [-1.1844; 2.6533]	-1.0821 [-2.5122; 0.3479]	1.2245 [ 0.5692; 1.8797]	rTMS	-1.0600 [-3.2002; 1.0802]
tDCS	1.0848 [-0.3896; 2.5591]	-0.1902 [-1.8337; 1.4533]	-1.7029 [-3.8156; 0.4098]	0.6571 [-1.2296; 2.5438]	-1.1595 [-2.5461; 0.2271]	1.1471 [ 0.5930; 1.7012]	-0.0774 [-0.9015; 0.7468]	tDCS

Code

# Treatment ranking
ranking_sr2 <- netrank(nma_sr2, small.values = "good")
print(ranking_sr2)

      P-score
Elec   0.9088
PbT    0.8536
CBT    0.5766
rTMS   0.5521
tDCS   0.5176
MfT    0.3088
Acu    0.1668
PlaSh  0.1157

Code

# Rankogram
ran_sr2 <- rankogram(nma_sr2)
plot(ran_sr2)

Code

# Design decomposition
decomp_sr2 <- decomp.design(nma_sr2)
print(decomp_sr2)

Q statistics to assess homogeneity / consistency

                    Q df  p-value
Total           68.71 22 < 0.0001
Within designs  68.09 21 < 0.0001
Between designs  0.62  1   0.4322

Design-specific decomposition of within-designs Q statistic

     Design     Q df  p-value
 PlaSh:tDCS 37.81 10 < 0.0001
 PlaSh:rTMS 26.38  8   0.0009
  PlaSh:Acu  3.26  1   0.0710
  PlaSh:PbT  0.40  1   0.5269
  PlaSh:CBT  0.24  1   0.6223

Between-designs Q statistic after detaching of single designs
(influential designs have p-value markedly different from 0.4322)

 Detached design    Q df p-value
      PlaSh:rTMS 0.00  0      --
      PlaSh:tDCS 0.00  0      --
       rTMS:tDCS 0.00  0      --

Q statistic to assess consistency under the assumption of
a full design-by-treatment interaction random effects model

                   Q df p-value tau.within tau2.within
Between designs 0.94  1  0.3330     0.7642      0.5841

Code

# Node-splitting analysis to detect inconsistency
split_sr2 <- netsplit(nma_sr2)
render_netsplit_table(split_sr2)

Table 4: Node-splitting analysis for Subnet 2

comparison	TE	seTE	lower	upper	statistic	p
Acu:CBT	1.2750	1.0532	-0.7891	3.3392	1.2106	0.2260
Acu:Elec	2.7877	1.2522	0.3334	5.2419	2.2262	0.0260
Acu:MfT	0.4277	1.1544	-1.8349	2.6902	0.3705	0.7110
Acu:PbT	2.2443	0.9521	0.3782	4.1103	2.3571	0.0184
Acu:PlaSh	-0.0623	0.6971	-1.4286	1.3039	-0.0894	0.9288
Acu:rTMS	1.1621	0.7731	-0.3532	2.6774	1.5032	0.1328
Acu:tDCS	1.0848	0.7522	-0.3896	2.5591	1.4421	0.1493
CBT:Elec	1.5127	1.3059	-1.0468	4.0721	1.1584	0.2467
CBT:MfT	-0.8473	1.2124	-3.2236	1.5289	-0.6989	0.4846
CBT:PbT	0.9692	1.0217	-1.0332	2.9717	0.9487	0.3428
CBT:PlaSh	-1.3373	0.7894	-2.8846	0.2099	-1.6940	0.0903
CBT:rTMS	-0.1129	0.8573	-1.7932	1.5674	-0.1317	0.8952
CBT:tDCS	-0.1902	0.8385	-1.8337	1.4533	-0.2269	0.8205
Elec:MfT	-2.3600	1.3888	-5.0820	0.3620	-1.6993	0.0893
Elec:PbT	-0.5434	1.2258	-2.9460	1.8591	-0.4433	0.6575
Elec:PlaSh	-2.8500	1.0402	-4.8888	-0.8112	-2.7398	0.0061
Elec:rTMS	-1.6255	1.0926	-3.7670	0.5159	-1.4878	0.1368
Elec:tDCS	-1.7029	1.0779	-3.8156	0.4098	-1.5798	0.1142
MfT:PbT	1.8166	1.1257	-0.3898	4.0230	1.6137	0.1066
MfT:PlaSh	-0.4900	0.9201	-2.2935	1.3135	-0.5325	0.5944
MfT:rTMS	0.7345	0.9790	-1.1844	2.6533	0.7502	0.4531
MfT:tDCS	0.6571	0.9626	-1.2296	2.5438	0.6826	0.4948
PbT:PlaSh	-2.3066	0.6485	-3.5777	-1.0355	-3.5567	0.0004
PbT:rTMS	-1.0821	0.7296	-2.5122	0.3479	-1.4831	0.1380
PbT:tDCS	-1.1595	0.7075	-2.5461	0.2271	-1.6389	0.1012
rTMS:PlaSh	-1.2245	0.3343	-1.8797	-0.5692	-3.6624	0.0002
tDCS:PlaSh	-1.1471	0.2827	-1.7012	-0.5930	-4.0575	0.0000
rTMS:tDCS	-0.0774	0.4205	-0.9015	0.7468	-0.1840	0.8540

Code

# Forest plot of node-splitting
plot_netsplit_forest(split_sr2)

Figure 2: Forest plot of node-splitting for Subnet 2

Code

# Heatmap
netheat(nma_sr2)

Heatmap of contribution matrix for Subnet 2

Code

# Funnel plot
funnel(nma_sr2, order = "PlaSh", )

Code

# Read data
data_psh <- read_excel(
    "data/Banco de Dados_Rstudio (1).xlsx",
    sheet = "P | Sh"
) |>
    mutate(
        Mean1 = as.numeric(Mean1),
        Mean2 = as.numeric(Mean2),
        Mean3 = as.numeric(Mean3),
        SD1 = as.numeric(SD1),
        SD2 = as.numeric(SD2),
        SD3 = as.numeric(SD3),
    )


# Transform to contrast-based
pw <- pairwise(
    treat = list(Treat1, Treat2, Treat3),
    n = list(N1, N2, N3),
    mean = list(Mean1, Mean2, Mean3),
    sd = list(SD1, SD2, SD3),
    studlab = StudyID,
    data = data_psh,
    sm = "MD"
)

# Check network connections
net_con <- netconnection(pw)
net_con

Number of studies: k = 120
Number of pairwise comparisons: m = 134
Number of treatments: n = 23
Number of designs: d = 53
Number of networks: 1

There are network:

Network:
- 120 studies
- 134 comparisons
- 23 treatments

Tip

The network is fully connected.

1.0.1 Network

Select the procedures performed

Important

The network contain 106 studies, 120 comparisons and 24 treatments.

Code

# Filter data for network
sr <- tibble(
    treat1 = net_con$treat1,
    treat2 = net_con$treat2,
    subnet = net_con$subnet
)


# Display comparison table
sr

Code

# Unique treatments in network
unique_treat_sr1 <- sr$treat1 |> unique()
print(unique_treat_sr1)

 [1] "Mnt"  "McT"  "WBV"  "MfT"  "AqET" "CBT"  "ReET" "Bal"  "Acu"  "AeET"
[11] "FlET" "MiET" "MasT" "PBT"  "rTMS" "tDCS" "DryN" "Elec" "HtT"  "Cry"

Code

unique_treat_sr2 <- sr$treat2 |> unique()
print(unique_treat_sr2)

 [1] "WlNi"  "WBV"   "PlaSh" "Bal"   "FlET"  "AqET"  "ReET"  "Mnt"   "CBT"  
[10] "McT"   "MasT"  "Elec"  "MiET"  "Plt"   "HtT"

Code

# Combination of all unique treatments
trat_sr <- unique(c(unique_treat_sr1, unique_treat_sr2))
print(trat_sr)

 [1] "Mnt"   "McT"   "WBV"   "MfT"   "AqET"  "CBT"   "ReET"  "Bal"   "Acu"  
[10] "AeET"  "FlET"  "MiET"  "MasT"  "PBT"   "rTMS"  "tDCS"  "DryN"  "Elec" 
[19] "HtT"   "Cry"   "WlNi"  "PlaSh" "Plt"

Code

# Total number of treatments
length(trat_sr)

[1] 23

Code

# Filter pairwise object only for comparisons within network
pw_psh <- pw %>%
    filter(treat1 %in% trat_sr & treat2 %in% trat_sr)

# Number of comparisons in network
nrow(pw_psh)

[1] 134

Code

# Fit NMA model for network
nma_sr <- netmeta(
    TE, seTE, treat1, treat2, studlab,
    data = pw_psh,
    n1 = N1,
    n2 = N2,
    random = TRUE,
    common = FALSE,
    sm = "MD",
    ref = "WlNi" # Reference treatment (change if you want another control)
)

# Basic model summary
print(nma_sr)

Number of studies: k = 120
Number of pairwise comparisons: m = 134
Number of observations: o = 7226
Number of treatments: n = 23
Number of designs: d = 53

Random effects model

Treatment estimate (other treatments vs 'WlNi'):
           MD             95% CI     z  p-value
Acu   -1.8647 [-3.0272; -0.7022] -3.14   0.0017
AeET  -1.3185 [-2.2337; -0.4033] -2.82   0.0047
AqET  -1.9221 [-2.9103; -0.9339] -3.81   0.0001
Bal   -2.9560 [-4.5388; -1.3731] -3.66   0.0003
CBT   -0.9412 [-1.4623; -0.4201] -3.54   0.0004
Cry   -1.9000 [-4.5027;  0.7027] -1.43   0.1525
DryN  -3.7642 [-5.3555; -2.1729] -4.64 < 0.0001
Elec  -2.1317 [-3.5514; -0.7119] -2.94   0.0033
FlET  -0.4010 [-1.4761;  0.6740] -0.73   0.4647
HtT   -1.3205 [-3.8145;  1.1736] -1.04   0.2994
MasT  -2.1591 [-3.2932; -1.0250] -3.73   0.0002
McT   -1.4311 [-2.0998; -0.7624] -4.19 < 0.0001
MfT   -2.3585 [-3.8527; -0.8644] -3.09   0.0020
MiET  -0.8528 [-1.6332; -0.0723] -2.14   0.0322
Mnt   -1.9319 [-3.1969; -0.6668] -2.99   0.0028
PBT   -3.1322 [-4.6425; -1.6218] -4.06 < 0.0001
PlaSh -0.6731 [-1.5936;  0.2474] -1.43   0.1518
Plt   -2.2524 [-3.7652; -0.7396] -2.92   0.0035
ReET  -1.4418 [-2.5334; -0.3502] -2.59   0.0096
rTMS  -1.8191 [-3.1357; -0.5025] -2.71   0.0068
tDCS  -2.0562 [-3.5255; -0.5870] -2.74   0.0061
WBV   -1.8112 [-3.5279; -0.0945] -2.07   0.0387
WlNi        .                  .     .        .

Quantifying heterogeneity / inconsistency:
tau^2 = 1.4538; tau = 1.2057; I^2 = 86.8% [84.5%; 88.7%]

Tests of heterogeneity (within designs) and inconsistency (between designs):
                     Q d.f.  p-value
Total           793.25  105 < 0.0001
Within designs  512.58   67 < 0.0001
Between designs 280.67   38 < 0.0001

Details of network meta-analysis methods:
- Frequentist graph-theoretical approach
- DerSimonian-Laird estimator for tau^2
- Calculation of I^2 based on Q

Code

# Network plot
netgraph(nma_sr,
    seq = "optimal",
    number.of.studies = TRUE,
    cex.points = n.trts,
    labels = paste0(trts, "\n(n=", round(n.trts), ")")
)

Network plot of treatments for Network (P | Sh)

Code

# Detailed model summary
summary(nma_sr)

Original data (with adjusted standard errors for multi-arm studies):

                        treat1 treat2      TE   seTE seTE.adj narms multiarm
Albers2018                 Mnt   WlNi -2.0900 0.6365   1.3634     2         
Alentorn-Geli2008          McT    WBV  1.8100 0.7468   1.7109     3        *
Alentorn-Geli2008          McT   WlNi -1.2900 0.7892   1.7639     3        *
Alentorn-Geli2008          WBV   WlNi -3.1000 0.8292   1.8215     3        *
Alfano2001                 MfT  PlaSh -0.6200 0.5434   1.3225     2         
Alptug2023                 Mnt   WlNi -3.4000 0.6919   1.3901     2         
Altan2004                 AqET    Bal  0.1800 0.6504   1.3700     2         
Ang2010                    CBT   WlNi  0.1000 0.6426   1.3663     2         
Arakaki2021               FlET   ReET  2.1800 0.8189   1.4575     2         
Ardic2007                  Bal   WlNi -4.2500 0.6931   1.3908     2         
Assefi2005                 Acu  PlaSh  0.6100 0.5069   1.3079     2         
Assis2006                 AeET   AqET  0.5000 0.5320   1.3179     2         
Assumpçao2018             FlET   ReET  0.2000 1.0224   1.9363     3        *
Assumpçao2018             FlET   WlNi -1.8000 1.0018   1.9047     3        *
Assumpçao2018             ReET   WlNi -2.0000 1.0408   1.9664     3        *
Atan2020                  MiET   WlNi -3.6000 0.4812   1.2982     2         
Audoux2023                MasT    Mnt -1.8000 0.8445   1.4721     2         
Baelz2022                  Acu  PlaSh -0.7000 0.8905   1.4989     2         
Barranengoa-Cuadra2021     CBT   WlNi -2.6000 0.3939   1.2684     2         
Bircan2008                AeET   ReET -0.4600 0.6518   1.3706     2         
Boggiss2022                CBT    PBT  4.0000 1.1997   1.7009     2         
Bongi2010                  CBT   WlNi -2.8300 0.5312   1.3175     2         
Bongi2012                  CBT    McT  0.6500 0.3294   1.2499     2         
Bourgault2015              McT   WlNi -0.1300 0.5563   1.3279     2         
Boyer2014                PlaSh   rTMS -1.2000 0.8003   1.4472     2         
Bressan2008               AeET   FlET  0.4700 1.1193   1.6452     2         
Brietzke2019             PlaSh   tDCS  2.4800 0.3776   1.2635     2         
Calandre2009              AqET   WlNi  0.0000 0.5134   1.3105     2         
Cao2020                    Acu   MasT -0.2200 0.3452   1.2542     2         
Carretero2009            PlaSh   rTMS -1.2000 0.8010   1.4475     2         
Carson2010                 McT   WlNi -1.0200 0.5934   1.3438     2         
Casanueva2014             DryN   WlNi -1.5000 0.3493   1.2553     2         
Castro-Sanchez2019        DryN   MasT -2.9300 0.4500   1.2870     2         
Castro-Sanchez2020        DryN   Elec -2.6800 0.4366   1.2823     2         
Caumo2023                PlaSh   tDCS  1.6600 0.1140   1.2111     2         
Ceballos-Laita2020         McT   MiET -2.1600 0.8359   1.4671     2         
Colbert1999                MfT  PlaSh -1.8100 0.8309   1.4643     2         
Collado-Mateo2017         MiET   WlNi -1.3300 0.4507   1.2872     2         
Coste2021                  Mnt  PlaSh -0.2900 0.6548   1.3720     2         
Da Costa2005              MiET   WlNi -0.9400 0.6066   1.3497     2         
Dailey2019                Elec  PlaSh -1.3000 0.3488   1.2552     2         
daSilva2008               AqET   Elec  3.2000 1.1908   1.6946     2         
deMedeiros2020            AqET    Plt -0.6000 0.6063   1.3496     2         
Ekici2008                 MasT    Plt  0.4200 0.4772   1.2967     2         
Ekici2017                 MasT    Plt  0.3800 0.5255   1.3153     2         
Espi-Lopes2016            MiET   WlNi -0.2800 0.9255   1.5200     2         
Evcik2002                  Bal   WlNi -3.4000 1.7701   2.1417     2         
Fernandes2016             AeET   AqET  0.5000 0.6362   1.3633     2         
Fitzgibbon2018           PlaSh   rTMS  0.4800 0.8369   1.4677     2         
Franco2023                AeET    Plt  1.2000 0.5585   1.3288     2         
Friedberg2019              CBT   WlNi -0.5500 0.3803   1.2643     2         
Giannotti2014              McT   WlNi -0.2500 0.8210   1.4587     2         
Goldway2019                CBT  PlaSh  0.8200 0.7901   1.4416     2         
Gomez-Hernandez2019       AeET   MiET  1.0100 0.1025   1.2101     2         
Gowans1999                 McT   WlNi -0.3000 0.6841   1.3863     2         
Gunther1994                Bal    CBT -1.1200 1.1934   1.6965     2         
Hargrove2012             PlaSh   tDCS  1.4000 0.6895   1.3889     2         
Harris2005                 Acu  PlaSh -0.3100 0.6954   1.3919     2         
Harte2013                  Acu  PlaSh  0.5800 0.5773   1.3368     2         
Hsu2010                    CBT   WlNi -0.5800 0.6751   1.3819     2         
Izquierdo-Alventosa2020   MiET   WlNi -0.1200 0.7797   1.4359     2         
Jamison2021               Elec  PlaSh -0.6200 0.2762   1.2370     2         
Jensen2012                 CBT   WlNi -1.0900 0.8702   1.4870     2         
Jones2002                  McT   ReET  0.5300 0.5654   1.3317     2         
Jones2012                  CBT    McT  1.1000 0.5460   1.3236     2         
Karatay2018                Acu  PlaSh -2.5200 0.5807   1.3383     2         
Kayo2012                  AeET   ReET -0.9700 0.7347   1.7427     3        *
Kayo2012                  AeET   WlNi -1.6000 0.7111   1.7123     3        *
Kayo2012                  ReET   WlNi -0.6300 0.6963   1.6944     3        *
Lami2018                   CBT   WlNi -0.0800 0.3222   1.2480     2         
Lauche2016                MasT  PlaSh -0.9200 0.4103   1.2736     2         
Lee2024                    CBT    McT -0.5000 0.3579   1.2577     2         
Lopes-Rodrigues2012       AqET   FlET -2.5300 0.5685   1.3330     2         
Lopes-Rodrigues2013       AqET   FlET -2.1600 0.4565   1.2893     2         
Luciano2014                CBT   WlNi -1.7700 0.2840   1.2387     2         
Lynch2012                  McT   WlNi -1.5700 0.3863   1.2661     2         
Maestu2013               PlaSh   rTMS  2.0000 0.6998   1.3941     2         
McCrae2019                 CBT   WlNi -0.4900 0.6214   1.3564     2         
Menzies2014                CBT   WlNi -0.5000 0.5384   1.3205     2         
Mhalla2011               PlaSh   rTMS  2.1200 0.3835   1.2652     2         
Mingorance2021.2           WBV   WlNi -0.5100 0.1736   1.2182     2         
Mist2018                   Acu    CBT -1.6000 0.1918   1.2209     2         
Nadal-Nicolas2020         MasT  PlaSh -2.9000 0.9876   1.5586     2         
Norrengaard1997           AeET    HtT  1.0000 1.1510   2.1676     3        *
Norrengaard1997           AeET   MiET  1.0000 1.1426   2.1443     3        *
Norrengaard1997            HtT   MiET  0.0000 0.5714   1.4839     3        *
Oka2019                    MfT  PlaSh -0.5500 0.6779   1.3832     2         
Paolucci2016               MfT  PlaSh -2.5000 0.4369   1.2825     2         
Paolucci2022               CBT   MiET -1.5000 0.9648   1.5442     2         
Park2021                  FlET   ReET -0.0400 0.6996   1.3940     2         
Parra-Delgado2013          CBT   WlNi -0.0600 0.1291   1.2126     2         
Redondo2004                CBT   MiET  0.4000 0.8083   1.4516     2         
Rivera2018                 Cry   WlNi -1.9000 0.5565   1.3279     2         
Rodriguez-Mansilla2021     McT   MiET -0.6300 0.5229   1.6101     3        *
Rodriguez-Mansilla2021    MiET   WlNi -0.5200 0.4364   1.5373     3        *
Rodriguez-Mansilla2021     McT   WlNi -1.1500 0.5830   1.6766     3        *
Ruaro2014                  PBT  PlaSh -2.3000 0.5385   1.3205     2         
Samartin-Veiga2022       PlaSh   tDCS  0.3100 0.7259   1.4074     2         
Sarmento2020               McT  PlaSh -3.7000 0.8544   1.4778     2         
Schachter2003             AeET   WlNi -1.2600 0.5078   1.3083     2         
Schulze2023               FlET   MasT  2.4500 0.2841   1.5181     3        *
Schulze2023               MasT   WlNi -2.6800 0.2735   1.5123     3        *
Schulze2023               FlET   WlNi -0.2300 0.2841   1.5181     3        *
Sencan2004                AeET  PlaSh -2.1500 0.4530   1.2880     2         
Sevimli2015               AeET   AqET  0.0200 0.2773   1.5040     3        *
Sevimli2015               AeET   MiET -2.4200 0.3318   1.5362     3        *
Sevimli2015               AqET   MiET -2.4400 0.3388   1.5409     3        *
Silva2019                  CBT   ReET  1.0400 0.5562   1.3278     2         
Sutbeyaz2009               MfT  PlaSh -2.7600 0.4486   1.2865     2         
Tanwar2020               PlaSh   rTMS  3.9000 0.2989   1.2422     2         
To2017                   PlaSh   tDCS  0.8100 0.4671   1.2930     2         
Tomas-Carus2007b&c        AqET   WlNi -2.0000 0.7472   1.4185     2         
Torres2015                 CBT    Mnt  1.6700 0.5133   1.3105     2         
Udina-Cortés2020          Elec  PlaSh -1.9000 0.5940   1.3441     2         
Ugurlu2017                 Acu  PlaSh -2.8900 0.4682   1.2934     2         
Valim2003                 AeET   FlET  0.3000 0.6728   1.3808     2         
Vas2016                    Acu  PlaSh -1.4800 0.3803   1.2643     2         
Verkaik2013                CBT   WlNi  0.0400 0.5154   1.3113     2         
Wicksell2013               CBT   WlNi -0.4000 0.3706   1.2614     2         
Wong2018                   McT   WlNi -1.7000 0.5833   1.3394     2         
Anderson2025               CBT  PlaSh  0.0000 0.8406   1.4698     2         
Catella2024                CBT   WlNi -0.5000 0.4424   1.2843     2         
Erol2025                   CBT   WlNi -2.8000 0.6172   1.3545     2         
Fernandes2024             FlET   ReET  1.8000 0.4659   1.2926     2         
Ferres2025                 McT   WlNi -0.8000 0.4742   1.2956     2         
Gendreau2024               CBT  PlaSh -0.7000 0.2111   1.2241     2         
Karakoyun2025              CBT   WlNi -4.1000 0.2475   1.2309     2         
Nhu2024                    CBT   WlNi  0.4000 0.6602   1.3746     2         
Ribeiro2023                PBT  PlaSh -1.9000 0.4544   1.2885     2         
RodriguezDominguez2026     McT   MiET -1.0000 0.5477   1.3243     2         
Sari2025                   McT   WlNi -4.0000 1.0466   1.5966     2         
Silva2025                PlaSh   rTMS  0.8000 0.4788   1.2973     2         
Tiwari2024               PlaSh   rTMS  1.0000 0.6458   1.3678     2         
Vassao2024                 PBT   WlNi -2.7000 0.8583   1.4800     2         

Number of treatment arms per study (by decreasing number of arms):
                        narms multiarm
Alentorn-Geli2008           3        *
Assumpçao2018               3        *
Kayo2012                    3        *
Norrengaard1997             3        *
Rodriguez-Mansilla2021      3        *
Schulze2023                 3        *
Sevimli2015                 3        *
Albers2018                  2         
Alfano2001                  2         
Alptug2023                  2         
Altan2004                   2         
Ang2010                     2         
Arakaki2021                 2         
Ardic2007                   2         
Assefi2005                  2         
Assis2006                   2         
Atan2020                    2         
Audoux2023                  2         
Baelz2022                   2         
Barranengoa-Cuadra2021      2         
Bircan2008                  2         
Boggiss2022                 2         
Bongi2010                   2         
Bongi2012                   2         
Bourgault2015               2         
Boyer2014                   2         
Bressan2008                 2         
Brietzke2019                2         
Calandre2009                2         
Cao2020                     2         
Carretero2009               2         
Carson2010                  2         
Casanueva2014               2         
Castro-Sanchez2019          2         
Castro-Sanchez2020          2         
Caumo2023                   2         
Ceballos-Laita2020          2         
Colbert1999                 2         
Collado-Mateo2017           2         
Coste2021                   2         
Da Costa2005                2         
Dailey2019                  2         
daSilva2008                 2         
deMedeiros2020              2         
Ekici2008                   2         
Ekici2017                   2         
Espi-Lopes2016              2         
Evcik2002                   2         
Fernandes2016               2         
Fitzgibbon2018              2         
Franco2023                  2         
Friedberg2019               2         
Giannotti2014               2         
Goldway2019                 2         
Gomez-Hernandez2019         2         
Gowans1999                  2         
Gunther1994                 2         
Hargrove2012                2         
Harris2005                  2         
Harte2013                   2         
Hsu2010                     2         
Izquierdo-Alventosa2020     2         
Jamison2021                 2         
Jensen2012                  2         
Jones2002                   2         
Jones2012                   2         
Karatay2018                 2         
Lami2018                    2         
Lauche2016                  2         
Lee2024                     2         
Lopes-Rodrigues2012         2         
Lopes-Rodrigues2013         2         
Luciano2014                 2         
Lynch2012                   2         
Maestu2013                  2         
McCrae2019                  2         
Menzies2014                 2         
Mhalla2011                  2         
Mingorance2021.2            2         
Mist2018                    2         
Nadal-Nicolas2020           2         
Oka2019                     2         
Paolucci2016                2         
Paolucci2022                2         
Park2021                    2         
Parra-Delgado2013           2         
Redondo2004                 2         
Rivera2018                  2         
Ruaro2014                   2         
Samartin-Veiga2022          2         
Sarmento2020                2         
Schachter2003               2         
Sencan2004                  2         
Silva2019                   2         
Sutbeyaz2009                2         
Tanwar2020                  2         
To2017                      2         
Tomas-Carus2007b&c          2         
Torres2015                  2         
Udina-Cortés2020            2         
Ugurlu2017                  2         
Valim2003                   2         
Vas2016                     2         
Verkaik2013                 2         
Wicksell2013                2         
Wong2018                    2         
Anderson2025                2         
Catella2024                 2         
Erol2025                    2         
Fernandes2024               2         
Ferres2025                  2         
Gendreau2024                2         
Karakoyun2025               2         
Nhu2024                     2         
Ribeiro2023                 2         
RodriguezDominguez2026      2         
Sari2025                    2         
Silva2025                   2         
Tiwari2024                  2         
Vassao2024                  2         

Results (random effects model):

                        treat1 treat2      MD             95% CI
Albers2018                 Mnt   WlNi -1.9319 [-3.1969; -0.6668]
Alentorn-Geli2008          McT    WBV  0.3801 [-1.3981;  2.1584]
Alentorn-Geli2008          McT   WlNi -1.4311 [-2.0998; -0.7624]
Alentorn-Geli2008          WBV   WlNi -1.8112 [-3.5279; -0.0945]
Alfano2001                 MfT  PlaSh -1.6854 [-2.8624; -0.5085]
Alptug2023                 Mnt   WlNi -1.9319 [-3.1969; -0.6668]
Altan2004                 AqET    Bal  1.0339 [-0.6431;  2.7109]
Ang2010                    CBT   WlNi -0.9412 [-1.4623; -0.4201]
Arakaki2021               FlET   ReET  1.0408 [-0.0560;  2.1376]
Ardic2007                  Bal   WlNi -2.9560 [-4.5388; -1.3731]
Assefi2005                 Acu  PlaSh -1.1916 [-2.0765; -0.3066]
Assis2006                 AeET   AqET  0.6036 [-0.4105;  1.6177]
Assumpçao2018             FlET   ReET  1.0408 [-0.0560;  2.1376]
Assumpçao2018             FlET   WlNi -0.4010 [-1.4761;  0.6740]
Assumpçao2018             ReET   WlNi -1.4418 [-2.5334; -0.3502]
Atan2020                  MiET   WlNi -0.8528 [-1.6332; -0.0723]
Audoux2023                MasT    Mnt -0.2272 [-1.7147;  1.2602]
Baelz2022                  Acu  PlaSh -1.1916 [-2.0765; -0.3066]
Barranengoa-Cuadra2021     CBT   WlNi -0.9412 [-1.4623; -0.4201]
Bircan2008                AeET   ReET  0.1233 [-1.0388;  1.2855]
Boggiss2022                CBT    PBT  2.1910 [ 0.6815;  3.7004]
Bongi2010                  CBT   WlNi -0.9412 [-1.4623; -0.4201]
Bongi2012                  CBT    McT  0.4899 [-0.2648;  1.2445]
Bourgault2015              McT   WlNi -1.4311 [-2.0998; -0.7624]
Boyer2014                PlaSh   rTMS  1.1460 [ 0.2046;  2.0874]
Bressan2008               AeET   FlET -0.9175 [-2.0253;  0.1904]
Brietzke2019             PlaSh   tDCS  1.3831 [ 0.2380;  2.5283]
Calandre2009              AqET   WlNi -1.9221 [-2.9103; -0.9339]
Cao2020                    Acu   MasT  0.2944 [-0.9841;  1.5730]
Carretero2009            PlaSh   rTMS  1.1460 [ 0.2046;  2.0874]
Carson2010                 McT   WlNi -1.4311 [-2.0998; -0.7624]
Casanueva2014             DryN   WlNi -3.7642 [-5.3555; -2.1729]
Castro-Sanchez2019        DryN   MasT -1.6051 [-3.2436;  0.0334]
Castro-Sanchez2020        DryN   Elec -1.6326 [-3.3442;  0.0791]
Caumo2023                PlaSh   tDCS  1.3831 [ 0.2380;  2.5283]
Ceballos-Laita2020         McT   MiET -0.5783 [-1.4877;  0.3311]
Colbert1999                MfT  PlaSh -1.6854 [-2.8624; -0.5085]
Collado-Mateo2017         MiET   WlNi -0.8528 [-1.6332; -0.0723]
Coste2021                  Mnt  PlaSh -1.2588 [-2.6311;  0.1136]
Da Costa2005              MiET   WlNi -0.8528 [-1.6332; -0.0723]
Dailey2019                Elec  PlaSh -1.4586 [-2.7024; -0.2147]
daSilva2008               AqET   Elec  0.2096 [-1.3549;  1.7740]
deMedeiros2020            AqET    Plt  0.3303 [-1.2052;  1.8657]
Ekici2008                 MasT    Plt  0.0933 [-1.3304;  1.5169]
Ekici2017                 MasT    Plt  0.0933 [-1.3304;  1.5169]
Espi-Lopes2016            MiET   WlNi -0.8528 [-1.6332; -0.0723]
Evcik2002                  Bal   WlNi -2.9560 [-4.5388; -1.3731]
Fernandes2016             AeET   AqET  0.6036 [-0.4105;  1.6177]
Fitzgibbon2018           PlaSh   rTMS  1.1460 [ 0.2046;  2.0874]
Franco2023                AeET    Plt  0.9339 [-0.5776;  2.4454]
Friedberg2019              CBT   WlNi -0.9412 [-1.4623; -0.4201]
Giannotti2014              McT   WlNi -1.4311 [-2.0998; -0.7624]
Goldway2019                CBT  PlaSh -0.2681 [-1.1720;  0.6358]
Gomez-Hernandez2019       AeET   MiET -0.4657 [-1.4750;  0.5436]
Gowans1999                 McT   WlNi -1.4311 [-2.0998; -0.7624]
Gunther1994                Bal    CBT -2.0147 [-3.6337; -0.3957]
Hargrove2012             PlaSh   tDCS  1.3831 [ 0.2380;  2.5283]
Harris2005                 Acu  PlaSh -1.1916 [-2.0765; -0.3066]
Harte2013                  Acu  PlaSh -1.1916 [-2.0765; -0.3066]
Hsu2010                    CBT   WlNi -0.9412 [-1.4623; -0.4201]
Izquierdo-Alventosa2020   MiET   WlNi -0.8528 [-1.6332; -0.0723]
Jamison2021               Elec  PlaSh -1.4586 [-2.7024; -0.2147]
Jensen2012                 CBT   WlNi -0.9412 [-1.4623; -0.4201]
Jones2002                  McT   ReET  0.0107 [-1.1797;  1.2011]
Jones2012                  CBT    McT  0.4899 [-0.2648;  1.2445]
Karatay2018                Acu  PlaSh -1.1916 [-2.0765; -0.3066]
Kayo2012                  AeET   ReET  0.1233 [-1.0388;  1.2855]
Kayo2012                  AeET   WlNi -1.3185 [-2.2337; -0.4033]
Kayo2012                  ReET   WlNi -1.4418 [-2.5334; -0.3502]
Lami2018                   CBT   WlNi -0.9412 [-1.4623; -0.4201]
Lauche2016                MasT  PlaSh -1.4860 [-2.6033; -0.3686]
Lee2024                    CBT    McT  0.4899 [-0.2648;  1.2445]
Lopes-Rodrigues2012       AqET   FlET -1.5211 [-2.6658; -0.3763]
Lopes-Rodrigues2013       AqET   FlET -1.5211 [-2.6658; -0.3763]
Luciano2014                CBT   WlNi -0.9412 [-1.4623; -0.4201]
Lynch2012                  McT   WlNi -1.4311 [-2.0998; -0.7624]
Maestu2013               PlaSh   rTMS  1.1460 [ 0.2046;  2.0874]
McCrae2019                 CBT   WlNi -0.9412 [-1.4623; -0.4201]
Menzies2014                CBT   WlNi -0.9412 [-1.4623; -0.4201]
Mhalla2011               PlaSh   rTMS  1.1460 [ 0.2046;  2.0874]
Mingorance2021.2           WBV   WlNi -1.8112 [-3.5279; -0.0945]
Mist2018                   Acu    CBT -0.9234 [-2.0648;  0.2179]
Nadal-Nicolas2020         MasT  PlaSh -1.4860 [-2.6033; -0.3686]
Norrengaard1997           AeET    HtT  0.0020 [-2.4944;  2.4983]
Norrengaard1997           AeET   MiET -0.4657 [-1.4750;  0.5436]
Norrengaard1997            HtT   MiET -0.4677 [-2.8891;  1.9537]
Oka2019                    MfT  PlaSh -1.6854 [-2.8624; -0.5085]
Paolucci2016               MfT  PlaSh -1.6854 [-2.8624; -0.5085]
Paolucci2022               CBT   MiET -0.0885 [-0.9554;  0.7785]
Park2021                  FlET   ReET  1.0408 [-0.0560;  2.1376]
Parra-Delgado2013          CBT   WlNi -0.9412 [-1.4623; -0.4201]
Redondo2004                CBT   MiET -0.0885 [-0.9554;  0.7785]
Rivera2018                 Cry   WlNi -1.9000 [-4.5027;  0.7027]
Rodriguez-Mansilla2021     McT   MiET -0.5783 [-1.4877;  0.3311]
Rodriguez-Mansilla2021    MiET   WlNi -0.8528 [-1.6332; -0.0723]
Rodriguez-Mansilla2021     McT   WlNi -1.4311 [-2.0998; -0.7624]
Ruaro2014                  PBT  PlaSh -2.4591 [-3.8973; -1.0209]
Samartin-Veiga2022       PlaSh   tDCS  1.3831 [ 0.2380;  2.5283]
Sarmento2020               McT  PlaSh -0.7580 [-1.8126;  0.2966]
Schachter2003             AeET   WlNi -1.3185 [-2.2337; -0.4033]
Schulze2023               FlET   MasT  1.7581 [ 0.3805;  3.1356]
Schulze2023               MasT   WlNi -2.1591 [-3.2932; -1.0250]
Schulze2023               FlET   WlNi -0.4010 [-1.4761;  0.6740]
Sencan2004                AeET  PlaSh -0.6454 [-1.7821;  0.4913]
Sevimli2015               AeET   AqET  0.6036 [-0.4105;  1.6177]
Sevimli2015               AeET   MiET -0.4657 [-1.4750;  0.5436]
Sevimli2015               AqET   MiET -1.0693 [-2.1995;  0.0609]
Silva2019                  CBT   ReET  0.5006 [-0.6378;  1.6389]
Sutbeyaz2009               MfT  PlaSh -1.6854 [-2.8624; -0.5085]
Tanwar2020               PlaSh   rTMS  1.1460 [ 0.2046;  2.0874]
To2017                   PlaSh   tDCS  1.3831 [ 0.2380;  2.5283]
Tomas-Carus2007b&c        AqET   WlNi -1.9221 [-2.9103; -0.9339]
Torres2015                 CBT    Mnt  0.9906 [-0.2962;  2.2775]
Udina-Cortés2020          Elec  PlaSh -1.4586 [-2.7024; -0.2147]
Ugurlu2017                 Acu  PlaSh -1.1916 [-2.0765; -0.3066]
Valim2003                 AeET   FlET -0.9175 [-2.0253;  0.1904]
Vas2016                    Acu  PlaSh -1.1916 [-2.0765; -0.3066]
Verkaik2013                CBT   WlNi -0.9412 [-1.4623; -0.4201]
Wicksell2013               CBT   WlNi -0.9412 [-1.4623; -0.4201]
Wong2018                   McT   WlNi -1.4311 [-2.0998; -0.7624]
Anderson2025               CBT  PlaSh -0.2681 [-1.1720;  0.6358]
Catella2024                CBT   WlNi -0.9412 [-1.4623; -0.4201]
Erol2025                   CBT   WlNi -0.9412 [-1.4623; -0.4201]
Fernandes2024             FlET   ReET  1.0408 [-0.0560;  2.1376]
Ferres2025                 McT   WlNi -1.4311 [-2.0998; -0.7624]
Gendreau2024               CBT  PlaSh -0.2681 [-1.1720;  0.6358]
Karakoyun2025              CBT   WlNi -0.9412 [-1.4623; -0.4201]
Nhu2024                    CBT   WlNi -0.9412 [-1.4623; -0.4201]
Ribeiro2023                PBT  PlaSh -2.4591 [-3.8973; -1.0209]
RodriguezDominguez2026     McT   MiET -0.5783 [-1.4877;  0.3311]
Sari2025                   McT   WlNi -1.4311 [-2.0998; -0.7624]
Silva2025                PlaSh   rTMS  1.1460 [ 0.2046;  2.0874]
Tiwari2024               PlaSh   rTMS  1.1460 [ 0.2046;  2.0874]
Vassao2024                 PBT   WlNi -3.1322 [-4.6425; -1.6218]

Number of studies: k = 120
Number of pairwise comparisons: m = 134
Number of observations: o = 7226
Number of treatments: n = 23
Number of designs: d = 53

Random effects model

Treatment estimate (other treatments vs 'WlNi'):
           MD             95% CI     z  p-value
Acu   -1.8647 [-3.0272; -0.7022] -3.14   0.0017
AeET  -1.3185 [-2.2337; -0.4033] -2.82   0.0047
AqET  -1.9221 [-2.9103; -0.9339] -3.81   0.0001
Bal   -2.9560 [-4.5388; -1.3731] -3.66   0.0003
CBT   -0.9412 [-1.4623; -0.4201] -3.54   0.0004
Cry   -1.9000 [-4.5027;  0.7027] -1.43   0.1525
DryN  -3.7642 [-5.3555; -2.1729] -4.64 < 0.0001
Elec  -2.1317 [-3.5514; -0.7119] -2.94   0.0033
FlET  -0.4010 [-1.4761;  0.6740] -0.73   0.4647
HtT   -1.3205 [-3.8145;  1.1736] -1.04   0.2994
MasT  -2.1591 [-3.2932; -1.0250] -3.73   0.0002
McT   -1.4311 [-2.0998; -0.7624] -4.19 < 0.0001
MfT   -2.3585 [-3.8527; -0.8644] -3.09   0.0020
MiET  -0.8528 [-1.6332; -0.0723] -2.14   0.0322
Mnt   -1.9319 [-3.1969; -0.6668] -2.99   0.0028
PBT   -3.1322 [-4.6425; -1.6218] -4.06 < 0.0001
PlaSh -0.6731 [-1.5936;  0.2474] -1.43   0.1518
Plt   -2.2524 [-3.7652; -0.7396] -2.92   0.0035
ReET  -1.4418 [-2.5334; -0.3502] -2.59   0.0096
rTMS  -1.8191 [-3.1357; -0.5025] -2.71   0.0068
tDCS  -2.0562 [-3.5255; -0.5870] -2.74   0.0061
WBV   -1.8112 [-3.5279; -0.0945] -2.07   0.0387
WlNi        .                  .     .        .

Quantifying heterogeneity / inconsistency:
tau^2 = 1.4538; tau = 1.2057; I^2 = 86.8% [84.5%; 88.7%]

Tests of heterogeneity (within designs) and inconsistency (between designs):
                     Q d.f.  p-value
Total           793.25  105 < 0.0001
Within designs  512.58   67 < 0.0001
Between designs 280.67   38 < 0.0001

Details of network meta-analysis methods:
- Frequentist graph-theoretical approach
- DerSimonian-Laird estimator for tau^2
- Calculation of I^2 based on Q

Code

# Forest plot
forest(nma_sr)

Forest plot of treatment effects for Network

Code

# Pairwise comparison table with confidence intervals
render_league_table(nma_sr)

Table 5: League table (random effects model) for Network

Comparison	Acu	AeET	AqET	Bal	CBT	Cry	DryN	Elec	FlET	HtT	MasT	McT	MfT	MiET	Mnt	PBT	PlaSh	Plt	ReET	rTMS	tDCS	WBV	WlNi
Acu	Acu	.	.	.	-1.6000 [-3.9929; 0.7929]	.	.	.	.	.	-0.2200 [-2.6781; 2.2381]	.	.	.	.	.	-0.9907 [-1.9848; 0.0034]	.	.	.	.	.	.
AeET	-0.5462 [-1.8957; 0.8033]	AeET	0.3225 [-1.1518; 1.7969]	.	.	.	.	.	0.3703 [-1.7027; 2.4432]	1.0000 [-2.2671; 4.2671]	.	.	.	-0.2940 [-1.8040; 1.2160]	.	.	-2.1500 [-4.6745; 0.3745]	1.2000 [-1.4044; 3.8044]	-0.7074 [-2.6350; 1.2201]	.	.	.	-1.4185 [-3.2919; 0.4548]
AqET	0.0574 [-1.3548; 1.4696]	0.6036 [-0.4105; 1.6177]	AqET	0.1800 [-2.5051; 2.8651]	.	.	.	3.2000 [-0.1214; 6.5214]	-2.3388 [-4.1552; -0.5225]	.	.	.	.	-2.4400 [-4.8947; 0.0147]	.	.	.	-0.6000 [-3.2452; 2.0452]	.	.	.	.	-0.9210 [-2.8076; 0.9656]
Bal	1.0913 [-0.8265; 3.0090]	1.6375 [-0.1105; 3.3855]	1.0339 [-0.6431; 2.7109]	Bal	-1.1200 [-4.4451; 2.2051]	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	-3.9979 [-6.2840; -1.7118]
CBT	-0.9234 [-2.0648; 0.2179]	-0.3773 [-1.3658; 0.6113]	-0.9809 [-2.0439; 0.0822]	-2.0147 [-3.6337; -0.3957]	CBT	.	.	.	.	.	.	0.3949 [-1.0488; 1.8386]	.	-0.4913 [-2.5643; 1.5816]	1.6700 [-0.8984; 4.2384]	4.0000 [ 0.6663; 7.3337]	-0.0450 [-1.5890; 1.4989]	.	1.0400 [-1.5625; 3.6425]	.	.	.	-1.0710 [-1.6915; -0.4504]
Cry	0.0353 [-2.8152; 2.8859]	0.5815 [-2.1774; 3.3404]	-0.0221 [-2.8061; 2.7619]	-1.0560 [-4.1022; 1.9903]	0.9588 [-1.6956; 3.6131]	Cry	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	-1.9000 [-4.5027; 0.7027]
DryN	1.8995 [ 0.1148; 3.6842]	2.4457 [ 0.6909; 4.2006]	1.8421 [ 0.0607; 3.6235]	0.8082 [-1.4044; 3.0209]	2.8230 [ 1.1926; 4.4533]	1.8642 [-1.1864; 4.9148]	DryN	-2.6800 [-5.1934; -0.1666]	.	.	-2.9300 [-5.4524; -0.4076]	.	.	.	.	.	.	.	.	.	.	.	-1.5000 [-3.9603; 0.9603]
Elec	0.2670 [-1.2303; 1.7643]	0.8132 [-0.7469; 2.3733]	0.2096 [-1.3549; 1.7740]	-0.8243 [-2.8952; 1.2466]	1.1904 [-0.2386; 2.6195]	0.2317 [-2.7331; 3.1964]	-1.6326 [-3.3442; 0.0791]	Elec	.	.	.	.	.	.	.	.	-1.2390 [-2.6832; 0.2052]	.	.	.	.	.	.
FlET	-1.4636 [-2.9402; 0.0129]	-0.9175 [-2.0253; 0.1904]	-1.5211 [-2.6658; -0.3763]	-2.5549 [-4.3886; -0.7213]	-0.5402 [-1.6797; 0.5993]	-1.4990 [-4.3150; 1.3170]	-3.3632 [-5.2027; -1.5236]	-1.7306 [-3.4006; -0.0607]	FlET	.	2.4500 [ 0.0221; 4.8779]	.	.	.	.	.	.	.	1.0902 [-0.3013; 2.4817]	.	.	.	-0.8335 [-2.7384; 1.0714]
HtT	-0.5442 [-3.2537; 2.1653]	0.0020 [-2.4944; 2.4983]	-0.6016 [-3.1970; 1.9938]	-1.6355 [-4.5588; 1.2878]	0.3792 [-2.1430; 2.9015]	-0.5795 [-4.1843; 3.0252]	-2.4437 [-5.3733; 0.4858]	-0.8112 [-3.6364; 2.0141]	0.9195 [-1.7195; 3.5584]	HtT	.	.	.	0.0000 [-2.6151; 2.6151]	.	.	.	.	.	.	.	.	.
MasT	0.2944 [-0.9841; 1.5730]	0.8406 [-0.4473; 2.1285]	0.2370 [-1.0993; 1.5733]	-0.7969 [-2.6920; 1.0982]	1.2179 [ 0.0521; 2.3836]	0.2591 [-2.5800; 3.0981]	-1.6051 [-3.2436; 0.0334]	0.0274 [-1.5261; 1.5810]	1.7581 [ 0.3805; 3.1356]	0.8386 [-1.8524; 3.5297]	MasT	.	.	.	-1.8000 [-4.6852; 1.0852]	.	-1.7128 [-3.6458; 0.2202]	0.4003 [-1.4096; 2.2101]	.	.	.	.	-2.6800 [-5.1032; -0.2568]
McT	-0.4336 [-1.7092; 0.8421]	0.1126 [-0.9532; 1.1784]	-0.4910 [-1.6342; 0.6522]	-1.5249 [-3.2225; 0.1727]	0.4899 [-0.2648; 1.2445]	-0.4689 [-3.1561; 2.2183]	-2.3331 [-4.0321; -0.6341]	-0.7006 [-2.2198; 0.8187]	1.0301 [-0.1772; 2.2373]	0.1106 [-2.4318; 2.6530]	-0.7280 [-1.9950; 0.5390]	McT	.	-1.2012 [-2.7441; 0.3417]	.	.	-3.7000 [-6.5964; -0.8036]	.	0.5300 [-2.0801; 3.1401]	.	.	1.8100 [-0.9697; 4.5897]	-1.1556 [-2.0054; -0.3058]
MfT	0.4939 [-0.9787; 1.9664]	1.0401 [-0.5962; 2.6764]	0.4365 [-1.2551; 2.1281]	-0.5974 [-2.7325; 1.5376]	1.4173 [-0.0667; 2.9013]	0.4585 [-2.5426; 3.4597]	-1.4057 [-3.4162; 0.6048]	0.2269 [-1.4855; 1.9393]	1.9575 [ 0.2074; 3.7076]	1.0381 [-1.8273; 3.9035]	0.1995 [-1.4234; 1.8223]	0.9274 [-0.6529; 2.5078]	MfT	.	.	.	-1.6854 [-2.8624; -0.5085]	.	.	.	.	.	.
MiET	-1.0119 [-2.3515; 0.3277]	-0.4657 [-1.4750; 0.5436]	-1.0693 [-2.1995; 0.0609]	-2.1032 [-3.8309; -0.3755]	-0.0885 [-0.9554; 0.7785]	-1.0472 [-3.7644; 1.6700]	-2.9114 [-4.6514; -1.1715]	-1.2789 [-2.8437; 0.2859]	0.4517 [-0.7807; 1.6842]	-0.4677 [-2.8891; 1.9537]	-1.3063 [-2.6196; 0.0070]	-0.5783 [-1.4877; 0.3311]	-1.5058 [-3.1398; 0.1282]	MiET	.	.	.	.	.	.	.	.	-1.2074 [-2.2909; -0.1238]
Mnt	0.0672 [-1.4882; 1.6225]	0.6134 [-0.8859; 2.1127]	0.0098 [-1.5384; 1.5580]	-1.0241 [-3.0241; 0.9759]	0.9906 [-0.2962; 2.2775]	0.0319 [-2.8620; 2.9257]	-1.8323 [-3.7703; 0.1056]	-0.1998 [-1.9719; 1.5723]	1.5308 [-0.0712; 3.1328]	0.6114 [-2.1629; 3.3856]	-0.2272 [-1.7147; 1.2602]	0.5008 [-0.8975; 1.8991]	-0.4267 [-2.2346; 1.3813]	1.0791 [-0.3757; 2.5339]	Mnt	.	-0.2900 [-2.9792; 2.3992]	.	.	.	.	.	-2.7323 [-4.6401; -0.8245]
PBT	1.2675 [-0.3863; 2.9213]	1.8137 [ 0.1219; 3.5055]	1.2101 [-0.5307; 2.9509]	0.1762 [-1.9807; 2.3332]	2.1910 [ 0.6815; 3.7004]	1.2322 [-1.7770; 4.2414]	-0.6320 [-2.7197; 1.4556]	1.0005 [-0.8627; 2.8638]	2.7312 [ 0.9365; 4.5258]	1.8117 [-1.0753; 4.6987]	0.9731 [-0.7608; 2.7070]	1.7011 [ 0.0905; 3.3117]	0.7737 [-1.0848; 2.6321]	2.2794 [ 0.6164; 3.9424]	1.2003 [-0.6727; 3.0734]	PBT	-2.0951 [-3.9026; -0.2876]	.	.	.	.	.	-2.7000 [-5.6008; 0.2008]
PlaSh	-1.1916 [-2.0765; -0.3066]	-0.6454 [-1.7821; 0.4913]	-1.2490 [-2.4640; -0.0340]	-2.2829 [-4.0642; -0.5015]	-0.2681 [-1.1720; 0.6358]	-1.2269 [-3.9876; 1.5338]	-3.0911 [-4.7211; -1.4611]	-1.4586 [-2.7024; -0.2147]	0.2721 [-1.0231; 1.5672]	-0.6474 [-3.2599; 1.9651]	-1.4860 [-2.6033; -0.3686]	-0.7580 [-1.8126; 0.2966]	-1.6854 [-2.8624; -0.5085]	-0.1797 [-1.3131; 0.9538]	-1.2588 [-2.6311; 0.1136]	-2.4591 [-3.8973; -1.0209]	PlaSh	.	.	1.1460 [ 0.2046; 2.0874]	1.3831 [ 0.2380; 2.5283]	.	.
Plt	0.3877 [-1.3313; 2.1067]	0.9339 [-0.5776; 2.4454]	0.3303 [-1.2052; 1.8657]	-0.7036 [-2.8154; 1.4082]	1.3111 [-0.2372; 2.8594]	0.3524 [-2.6581; 3.3628]	-1.5118 [-3.5336; 0.5099]	0.1207 [-1.7804; 2.0219]	1.8513 [ 0.1844; 3.5182]	0.9319 [-1.9068; 3.7706]	0.0933 [-1.3304; 1.5169]	0.8213 [-0.7928; 2.4353]	-0.1062 [-2.0777; 1.8654]	1.3996 [-0.2222; 3.0214]	0.3205 [-1.5378; 2.1788]	-0.8798 [-2.9205; 1.1608]	1.5793 [-0.0024; 3.1609]	Plt	.	.	.	.	.
ReET	-0.4229 [-1.9306; 1.0849]	0.1233 [-1.0388; 1.2855]	-0.4803 [-1.7626; 0.8021]	-1.5141 [-3.3818; 0.3535]	0.5006 [-0.6378; 1.6389]	-0.4582 [-3.2805; 2.3642]	-2.3224 [-4.1961; -0.4487]	-0.6898 [-2.3975; 1.0178]	1.0408 [-0.0560; 2.1376]	0.1213 [-2.5316; 2.7743]	-0.7173 [-2.1732; 0.7387]	0.0107 [-1.1797; 1.2011]	-0.9167 [-2.6919; 0.8584]	0.5890 [-0.6640; 1.8420]	-0.4900 [-2.1139; 1.1338]	-1.6904 [-3.5018; 0.1210]	0.7687 [-0.5601; 2.0976]	-0.8106 [-2.5435; 0.9224]	ReET	.	.	.	-1.2234 [-3.2780; 0.8312]
rTMS	-0.0456 [-1.3376; 1.2464]	0.5006 [-0.9753; 1.9765]	-0.1030 [-1.6400; 1.4340]	-1.1369 [-3.1516; 0.8779]	0.8779 [-0.4272; 2.1829]	-0.0809 [-2.9977; 2.8359]	-1.9451 [-3.8274; -0.0628]	-0.3126 [-1.8725; 1.2473]	1.4181 [-0.1831; 3.0192]	0.4986 [-2.2783; 3.2756]	-0.3400 [-1.8010; 1.1210]	0.3880 [-1.0257; 1.8016]	-0.5395 [-2.0466; 0.9677]	0.9663 [-0.5071; 2.4397]	-0.1128 [-1.7770; 1.5514]	-1.3131 [-3.0320; 0.4058]	1.1460 [ 0.2046; 2.0874]	-0.4333 [-2.2739; 1.4073]	0.3773 [-1.2512; 2.0058]	rTMS	.	.	.
tDCS	0.1916 [-1.2557; 1.6388]	0.7378 [-0.8758; 2.3513]	0.1342 [-1.5354; 1.8037]	-0.8997 [-3.0174; 1.2179]	1.1150 [-0.3439; 2.5739]	0.1562 [-2.8325; 3.1450]	-1.7080 [-3.7000; 0.2840]	-0.0754 [-1.7661; 1.6153]	1.6552 [-0.0736; 3.3840]	0.7358 [-2.1167; 3.5882]	-0.1028 [-1.7028; 1.4971]	0.6251 [-0.9316; 2.1819]	-0.3023 [-1.9444; 1.3398]	1.2035 [-0.4077; 2.8147]	0.1244 [-1.6630; 1.9118]	-1.0760 [-2.9143; 0.7624]	1.3831 [ 0.2380; 2.5283]	-0.1961 [-2.1488; 1.7565]	0.6144 [-1.1398; 2.3686]	0.2372 [-1.2452; 1.7195]	tDCS	.	.
WBV	-0.0534 [-2.1160; 2.0091]	0.4928 [-1.4427; 2.4282]	-0.1109 [-2.0840; 1.8623]	-1.1447 [-3.4759; 1.1864]	0.8700 [-0.9132; 2.6532]	-0.0888 [-3.2066; 3.0291]	-1.9530 [-4.2886; 0.3827]	-0.3204 [-2.5391; 1.8983]	1.4102 [-0.6060; 3.4264]	0.4908 [-2.5283; 3.5098]	-0.3478 [-2.3972; 1.7015]	0.3801 [-1.3981; 2.1584]	-0.5473 [-2.8128; 1.7182]	0.9585 [-0.9115; 2.8284]	-0.1206 [-2.2475; 2.0062]	-1.3210 [-3.5999; 0.9579]	1.1381 [-0.7976; 3.0739]	-0.4411 [-2.7219; 1.8396]	0.3694 [-1.6508; 2.3896]	-0.0079 [-2.1604; 2.1447]	-0.2450 [-2.4941; 2.0041]	WBV	-1.5702 [-3.4051; 0.2648]
WlNi	-1.8647 [-3.0272; -0.7022]	-1.3185 [-2.2337; -0.4033]	-1.9221 [-2.9103; -0.9339]	-2.9560 [-4.5388; -1.3731]	-0.9412 [-1.4623; -0.4201]	-1.9000 [-4.5027; 0.7027]	-3.7642 [-5.3555; -2.1729]	-2.1317 [-3.5514; -0.7119]	-0.4010 [-1.4761; 0.6740]	-1.3205 [-3.8145; 1.1736]	-2.1591 [-3.2932; -1.0250]	-1.4311 [-2.0998; -0.7624]	-2.3585 [-3.8527; -0.8644]	-0.8528 [-1.6332; -0.0723]	-1.9319 [-3.1969; -0.6668]	-3.1322 [-4.6425; -1.6218]	-0.6731 [-1.5936; 0.2474]	-2.2524 [-3.7652; -0.7396]	-1.4418 [-2.5334; -0.3502]	-1.8191 [-3.1357; -0.5025]	-2.0562 [-3.5255; -0.5870]	-1.8112 [-3.5279; -0.0945]	WlNi

Code

# Treatment ranking
ranking_sr <- netrank(nma_sr, small.values = "good")
print(ranking_sr)

      P-score
DryN   0.9500
PBT    0.8716
Bal    0.8298
MfT    0.7013
Plt    0.6681
MasT   0.6530
Elec   0.6336
tDCS   0.6093
AqET   0.5782
Mnt    0.5727
Acu    0.5500
Cry    0.5407
rTMS   0.5319
WBV    0.5282
ReET   0.4094
McT    0.4019
HtT    0.3983
AeET   0.3590
CBT    0.2235
MiET   0.2051
PlaSh  0.1519
FlET   0.1057
WlNi   0.0268

Code

# Rankogram
ran_sr <- rankogram(nma_sr)
plot(ran_sr)

Code

# Design decomposition
decomp_sr <- decomp.design(nma_sr)
print(decomp_sr)

Q statistics to assess homogeneity / consistency

                     Q  df  p-value
Total           793.25 105 < 0.0001
Within designs  512.58  67 < 0.0001
Between designs 280.67  38 < 0.0001

Design-specific decomposition of within-designs Q statistic

     Design      Q df  p-value
   WlNi:CBT 280.65 16 < 0.0001
 PlaSh:rTMS  84.26  7 < 0.0001
  Acu:PlaSh  42.88  6 < 0.0001
  WlNi:MiET  24.29  4 < 0.0001
  MfT:PlaSh  15.09  4   0.0045
    CBT:McT   8.27  2   0.0160
 PlaSh:tDCS  11.61  4   0.0205
   WlNi:McT  16.11  7   0.0241
  WlNi:AqET   4.87  1   0.0274
  FlET:ReET   5.88  2   0.0528
 MasT:PlaSh   3.43  1   0.0641
 Elec:PlaSh   4.93  2   0.0851
   CBT:MiET   2.28  1   0.1312
  CBT:PlaSh   3.93  2   0.1399
   WlNi:Mnt   1.94  1   0.1635
   McT:MiET   1.35  1   0.2457
  PBT:PlaSh   0.32  1   0.5702
  AqET:FlET   0.26  1   0.6118
   WlNi:Bal   0.20  1   0.6548
  AeET:FlET   0.02  1   0.8964
   MasT:Plt   0.00  1   0.9551
  AeET:AqET   0.00  1   1.0000

Between-designs Q statistic after detaching of single designs
(influential designs have p-value markedly different from < 0.0001)

 Detached design      Q df  p-value
  AeET:AqET:MiET 190.52 36 < 0.0001
       AeET:MiET 193.41 37 < 0.0001
       WlNi:DryN 236.66 37 < 0.0001
       DryN:MasT 257.96 37 < 0.0001
      AeET:PlaSh 262.15 37 < 0.0001
        WlNi:WBV 266.72 37 < 0.0001
       Acu:PlaSh 268.01 37 < 0.0001
        McT:MiET 268.38 37 < 0.0001
       McT:PlaSh 269.00 37 < 0.0001
    WlNi:McT:WBV 266.71 36 < 0.0001
  WlNi:FlET:MasT 266.87 36 < 0.0001
         Acu:CBT 269.72 37 < 0.0001
       AqET:Elec 271.23 37 < 0.0001
       WlNi:AqET 272.61 37 < 0.0001
       DryN:Elec 273.16 37 < 0.0001
        WlNi:Bal 273.87 37 < 0.0001
        MasT:Mnt 275.02 37 < 0.0001
       AeET:FlET 275.17 37 < 0.0001
       Mnt:PlaSh 276.12 37 < 0.0001
        AqET:Bal 276.30 37 < 0.0001
        WlNi:Mnt 276.87 37 < 0.0001
   WlNi:McT:MiET 274.58 36 < 0.0001
         CBT:PBT 277.06 37 < 0.0001
       AqET:FlET 277.36 37 < 0.0001
        AqET:Plt 277.64 37 < 0.0001
        WlNi:CBT 277.85 37 < 0.0001
        CBT:MiET 278.31 37 < 0.0001
       AeET:ReET 278.58 37 < 0.0001
        MasT:Plt 278.67 37 < 0.0001
         CBT:Mnt 278.74 37 < 0.0001
        CBT:ReET 278.75 37 < 0.0001
        WlNi:McT 278.76 37 < 0.0001
  WlNi:AeET:ReET 276.68 36 < 0.0001
       WlNi:MiET 279.08 37 < 0.0001
      Elec:PlaSh 279.17 37 < 0.0001
       PBT:PlaSh 279.23 37 < 0.0001
        McT:ReET 279.29 37 < 0.0001
  WlNi:FlET:ReET 276.95 36 < 0.0001
       AeET:AqET 279.60 37 < 0.0001
       WlNi:AeET 279.90 37 < 0.0001
       CBT:PlaSh 279.93 37 < 0.0001
         Bal:CBT 280.04 37 < 0.0001
      MasT:PlaSh 280.06 37 < 0.0001
         CBT:McT 280.14 37 < 0.0001
       FlET:ReET 280.56 37 < 0.0001
        Acu:MasT 280.62 37 < 0.0001
        AeET:Plt 280.67 37 < 0.0001
        WlNi:PBT 280.67 37 < 0.0001

Q statistic to assess consistency under the assumption of
a full design-by-treatment interaction random effects model

                    Q df p-value tau.within tau2.within
Between designs 35.02 38  0.6079     1.2196      1.4875

Code

# Node-splitting analysis to detect inconsistency
split_sr <- netsplit(nma_sr)
render_netsplit_table(split_sr)

Table 6: Node-splitting analysis for Network

comparison	TE	seTE	lower	upper	statistic	p
Acu:AeET	-0.5462	0.6885	-1.8957	0.8033	-0.7933	0.4276
Acu:AqET	0.0574	0.7205	-1.3548	1.4696	0.0797	0.9365
Acu:Bal	1.0913	0.9785	-0.8265	3.0090	1.1153	0.2647
Acu:CBT	-0.9234	0.5823	-2.0648	0.2179	-1.5858	0.1128
Acu:Cry	0.0353	1.4544	-2.8152	2.8859	0.0243	0.9806
Acu:DryN	1.8995	0.9106	0.1148	3.6842	2.0861	0.0370
Acu:Elec	0.2670	0.7639	-1.2303	1.7643	0.3495	0.7267
Acu:FlET	-1.4636	0.7534	-2.9402	0.0129	-1.9428	0.0520
Acu:HtT	-0.5442	1.3824	-3.2537	2.1653	-0.3937	0.6938
Acu:MasT	0.2944	0.6523	-0.9841	1.5730	0.4513	0.6518
Acu:McT	-0.4336	0.6509	-1.7092	0.8421	-0.6662	0.5053
Acu:MfT	0.4939	0.7513	-0.9787	1.9664	0.6573	0.5110
Acu:MiET	-1.0119	0.6835	-2.3515	0.3277	-1.4805	0.1387
Acu:Mnt	0.0672	0.7936	-1.4882	1.6225	0.0847	0.9325
Acu:PBT	1.2675	0.8438	-0.3863	2.9213	1.5021	0.1331
Acu:PlaSh	-1.1916	0.4515	-2.0765	-0.3066	-2.6390	0.0083
Acu:Plt	0.3877	0.8770	-1.3313	2.1067	0.4420	0.6585
Acu:ReET	-0.4229	0.7693	-1.9306	1.0849	-0.5497	0.5825
Acu:rTMS	-0.0456	0.6592	-1.3376	1.2464	-0.0692	0.9449
Acu:tDCS	0.1916	0.7384	-1.2557	1.6388	0.2594	0.7953
Acu:WBV	-0.0534	1.0523	-2.1160	2.0091	-0.0508	0.9595
Acu:WlNi	-1.8647	0.5931	-3.0272	-0.7022	-3.1438	0.0017
AeET:AqET	0.6036	0.5174	-0.4105	1.6177	1.1665	0.2434
AeET:Bal	1.6375	0.8918	-0.1105	3.3855	1.8361	0.0663
AeET:CBT	-0.3773	0.5044	-1.3658	0.6113	-0.7480	0.4545
AeET:Cry	0.5815	1.4076	-2.1774	3.3404	0.4131	0.6795
AeET:DryN	2.4457	0.8954	0.6909	4.2006	2.7316	0.0063
AeET:Elec	0.8132	0.7960	-0.7469	2.3733	1.0216	0.3070
AeET:FlET	-0.9175	0.5653	-2.0253	0.1904	-1.6231	0.1046
AeET:HtT	0.0020	1.2737	-2.4944	2.4983	0.0016	0.9987
AeET:MasT	0.8406	0.6571	-0.4473	2.1285	1.2793	0.2008
AeET:McT	0.1126	0.5438	-0.9532	1.1784	0.2071	0.8359
AeET:MfT	1.0401	0.8349	-0.5962	2.6764	1.2458	0.2128
AeET:MiET	-0.4657	0.5149	-1.4750	0.5436	-0.9044	0.3658
AeET:Mnt	0.6134	0.7650	-0.8859	2.1127	0.8018	0.4226
AeET:PBT	1.8137	0.8632	0.1219	3.5055	2.1012	0.0356
AeET:PlaSh	-0.6454	0.5800	-1.7821	0.4913	-1.1128	0.2658
AeET:Plt	0.9339	0.7712	-0.5776	2.4454	1.2110	0.2259
AeET:ReET	0.1233	0.5930	-1.0388	1.2855	0.2080	0.8352
AeET:rTMS	0.5006	0.7530	-0.9753	1.9765	0.6648	0.5062
AeET:tDCS	0.7378	0.8232	-0.8758	2.3513	0.8962	0.3702
AeET:WBV	0.4928	0.9875	-1.4427	2.4282	0.4990	0.6178
AeET:WlNi	-1.3185	0.4669	-2.2337	-0.4033	-2.8236	0.0047
AqET:Bal	1.0339	0.8556	-0.6431	2.7109	1.2083	0.2269
AqET:CBT	-0.9809	0.5424	-2.0439	0.0822	-1.8085	0.0705
AqET:Cry	-0.0221	1.4204	-2.8061	2.7619	-0.0155	0.9876
AqET:DryN	1.8421	0.9089	0.0607	3.6235	2.0268	0.0427
AqET:Elec	0.2096	0.7982	-1.3549	1.7740	0.2626	0.7929
AqET:FlET	-1.5211	0.5841	-2.6658	-0.3763	-2.6043	0.0092
AqET:HtT	-0.6016	1.3242	-3.1970	1.9938	-0.4543	0.6496
AqET:MasT	0.2370	0.6818	-1.0993	1.5733	0.3476	0.7281
AqET:McT	-0.4910	0.5833	-1.6342	0.6522	-0.8418	0.3999
AqET:MfT	0.4365	0.8631	-1.2551	2.1281	0.5057	0.6131
AqET:MiET	-1.0693	0.5766	-2.1995	0.0609	-1.8544	0.0637
AqET:Mnt	0.0098	0.7899	-1.5384	1.5580	0.0124	0.9901
AqET:PBT	1.2101	0.8882	-0.5307	2.9509	1.3624	0.1731
AqET:PlaSh	-1.2490	0.6199	-2.4640	-0.0340	-2.0148	0.0439
AqET:Plt	0.3303	0.7834	-1.2052	1.8657	0.4216	0.6733
AqET:ReET	-0.4803	0.6543	-1.7626	0.8021	-0.7341	0.4629
AqET:rTMS	-0.1030	0.7842	-1.6400	1.4340	-0.1313	0.8955
AqET:tDCS	0.1342	0.8518	-1.5354	1.8037	0.1575	0.8749
AqET:WBV	-0.1109	1.0067	-2.0840	1.8623	-0.1101	0.9123
AqET:WlNi	-1.9221	0.5042	-2.9103	-0.9339	-3.8121	0.0001
Bal:CBT	-2.0147	0.8260	-3.6337	-0.3957	-2.4390	0.0147
Bal:Cry	-1.0560	1.5542	-4.1022	1.9903	-0.6794	0.4969
Bal:DryN	0.8082	1.1289	-1.4044	3.0209	0.7160	0.4740
Bal:Elec	-0.8243	1.0566	-2.8952	1.2466	-0.7802	0.4353
Bal:FlET	-2.5549	0.9356	-4.3886	-0.7213	-2.7309	0.0063
Bal:HtT	-1.6355	1.4915	-4.5588	1.2878	-1.0965	0.2728
Bal:MasT	-0.7969	0.9669	-2.6920	1.0982	-0.8241	0.4099
Bal:McT	-1.5249	0.8661	-3.2225	0.1727	-1.7605	0.0783
Bal:MfT	-0.5974	1.0893	-2.7325	1.5376	-0.5484	0.5834
Bal:MiET	-2.1032	0.8815	-3.8309	-0.3755	-2.3859	0.0170
Bal:Mnt	-1.0241	1.0204	-3.0241	0.9759	-1.0036	0.3156
Bal:PBT	0.1762	1.1005	-1.9807	2.3332	0.1601	0.8728
Bal:PlaSh	-2.2829	0.9089	-4.0642	-0.5015	-2.5118	0.0120
Bal:Plt	-0.7036	1.0775	-2.8154	1.4082	-0.6530	0.5137
Bal:ReET	-1.5141	0.9529	-3.3818	0.3535	-1.5890	0.1121
Bal:rTMS	-1.1369	1.0280	-3.1516	0.8779	-1.1060	0.2687
Bal:tDCS	-0.8997	1.0804	-3.0174	1.2179	-0.8327	0.4050
Bal:WBV	-1.1447	1.1894	-3.4759	1.1864	-0.9625	0.3358
Bal:WlNi	-2.9560	0.8076	-4.5388	-1.3731	-3.6602	0.0003
CBT:Cry	0.9588	1.3543	-1.6956	3.6131	0.7080	0.4790
CBT:DryN	2.8230	0.8318	1.1926	4.4533	3.3937	0.0007
CBT:Elec	1.1904	0.7291	-0.2386	2.6195	1.6327	0.1025
CBT:FlET	-0.5402	0.5814	-1.6797	0.5993	-0.9292	0.3528
CBT:HtT	0.3792	1.2869	-2.1430	2.9015	0.2947	0.7682
CBT:MasT	1.2179	0.5948	0.0521	2.3836	2.0476	0.0406
CBT:McT	0.4899	0.3850	-0.2648	1.2445	1.2723	0.2033
CBT:MfT	1.4173	0.7572	-0.0667	2.9013	1.8719	0.0612
CBT:MiET	-0.0885	0.4423	-0.9554	0.7785	-0.2000	0.8415
CBT:Mnt	0.9906	0.6566	-0.2962	2.2775	1.5088	0.1313
CBT:PBT	2.1910	0.7701	0.6815	3.7004	2.8449	0.0044
CBT:PlaSh	-0.2681	0.4612	-1.1720	0.6358	-0.5814	0.5610
CBT:Plt	1.3111	0.7900	-0.2372	2.8594	1.6597	0.0970
CBT:ReET	0.5006	0.5808	-0.6378	1.6389	0.8619	0.3888
CBT:rTMS	0.8779	0.6659	-0.4272	2.1829	1.3184	0.1874
CBT:tDCS	1.1150	0.7443	-0.3439	2.5739	1.4980	0.1341
CBT:WBV	0.8700	0.9098	-0.9132	2.6532	0.9562	0.3390
CBT:WlNi	-0.9412	0.2659	-1.4623	-0.4201	-3.5403	0.0004
Cry:DryN	1.8642	1.5565	-1.1864	4.9148	1.1977	0.2310
Cry:Elec	0.2317	1.5127	-2.7331	3.1964	0.1531	0.8783
Cry:FlET	-1.4990	1.4368	-4.3150	1.3170	-1.0433	0.2968
Cry:HtT	-0.5795	1.8392	-4.1843	3.0252	-0.3151	0.7527
Cry:MasT	0.2591	1.4485	-2.5800	3.0981	0.1789	0.8580
Cry:McT	-0.4689	1.3711	-3.1561	2.2183	-0.3420	0.7324
Cry:MfT	0.4585	1.5312	-2.5426	3.4597	0.2995	0.7646
Cry:MiET	-1.0472	1.3864	-3.7644	1.6700	-0.7554	0.4500
Cry:Mnt	0.0319	1.4765	-2.8620	2.9257	0.0216	0.9828
Cry:PBT	1.2322	1.5353	-1.7770	4.2414	0.8026	0.4222
Cry:PlaSh	-1.2269	1.4085	-3.9876	1.5338	-0.8710	0.3837
Cry:Plt	0.3524	1.5360	-2.6581	3.3628	0.2294	0.8185
Cry:ReET	-0.4582	1.4400	-3.2805	2.3642	-0.3182	0.7503
Cry:rTMS	-0.0809	1.4882	-2.9977	2.8359	-0.0544	0.9566
Cry:tDCS	0.1562	1.5249	-2.8325	3.1450	0.1025	0.9184
Cry:WBV	-0.0888	1.5908	-3.2066	3.0291	-0.0558	0.9555
Cry:WlNi	-1.9000	1.3279	-4.5027	0.7027	-1.4308	0.1525
DryN:Elec	-1.6326	0.8733	-3.3442	0.0791	-1.8694	0.0616
DryN:FlET	-3.3632	0.9386	-5.2027	-1.5236	-3.5833	0.0003
DryN:HtT	-2.4437	1.4947	-5.3733	0.4858	-1.6349	0.1021
DryN:MasT	-1.6051	0.8360	-3.2436	0.0334	-1.9200	0.0549
DryN:McT	-2.3331	0.8668	-4.0321	-0.6341	-2.6915	0.0071
DryN:MfT	-1.4057	1.0258	-3.4162	0.6048	-1.3703	0.1706
DryN:MiET	-2.9114	0.8878	-4.6514	-1.1715	-3.2795	0.0010
DryN:Mnt	-1.8323	0.9888	-3.7703	0.1056	-1.8532	0.0639
DryN:PBT	-0.6320	1.0651	-2.7197	1.4556	-0.5934	0.5529
DryN:PlaSh	-3.0911	0.8316	-4.7211	-1.4611	-3.7169	0.0002
DryN:Plt	-1.5118	1.0315	-3.5336	0.5099	-1.4656	0.1427
DryN:ReET	-2.3224	0.9560	-4.1961	-0.4487	-2.4293	0.0151
DryN:rTMS	-1.9451	0.9604	-3.8274	-0.0628	-2.0254	0.0428
DryN:tDCS	-1.7080	1.0163	-3.7000	0.2840	-1.6805	0.0929
DryN:WBV	-1.9530	1.1917	-4.2886	0.3827	-1.6389	0.1012
DryN:WlNi	-3.7642	0.8119	-5.3555	-2.1729	-4.6363	0.0000
Elec:FlET	-1.7306	0.8520	-3.4006	-0.0607	-2.0312	0.0422
Elec:HtT	-0.8112	1.4415	-3.6364	2.0141	-0.5627	0.5736
Elec:MasT	0.0274	0.7926	-1.5261	1.5810	0.0346	0.9724
Elec:McT	-0.7006	0.7751	-2.2198	0.8187	-0.9038	0.3661
Elec:MfT	0.2269	0.8737	-1.4855	1.9393	0.2597	0.7951
Elec:MiET	-1.2789	0.7984	-2.8437	0.2859	-1.6018	0.1092
Elec:Mnt	-0.1998	0.9041	-1.9719	1.5723	-0.2210	0.8251
Elec:PBT	1.0005	0.9507	-0.8627	2.8638	1.0525	0.2926
Elec:PlaSh	-1.4586	0.6346	-2.7024	-0.2147	-2.2983	0.0215
Elec:Plt	0.1207	0.9700	-1.7804	2.0219	0.1244	0.9010
Elec:ReET	-0.6898	0.8713	-2.3975	1.0178	-0.7918	0.4285
Elec:rTMS	-0.3126	0.7959	-1.8725	1.2473	-0.3927	0.6945
Elec:tDCS	-0.0754	0.8626	-1.7661	1.6153	-0.0874	0.9303
Elec:WBV	-0.3204	1.1320	-2.5391	1.8983	-0.2831	0.7771
Elec:WlNi	-2.1317	0.7244	-3.5514	-0.7119	-2.9428	0.0033
FlET:HtT	0.9195	1.3464	-1.7195	3.5584	0.6829	0.4947
FlET:MasT	1.7581	0.7029	0.3805	3.1356	2.5013	0.0124
FlET:McT	1.0301	0.6160	-0.1772	2.2373	1.6723	0.0945
FlET:MfT	1.9575	0.8929	0.2074	3.7076	2.1923	0.0284
FlET:MiET	0.4517	0.6288	-0.7807	1.6842	0.7184	0.4725
FlET:Mnt	1.5308	0.8174	-0.0712	3.1328	1.8729	0.0611
FlET:PBT	2.7312	0.9157	0.9365	4.5258	2.9827	0.0029
FlET:PlaSh	0.2721	0.6608	-1.0231	1.5672	0.4117	0.6805
FlET:Plt	1.8513	0.8505	0.1844	3.5182	2.1768	0.0295
FlET:ReET	1.0408	0.5596	-0.0560	2.1376	1.8599	0.0629
FlET:rTMS	1.4181	0.8169	-0.1831	3.0192	1.7359	0.0826
FlET:tDCS	1.6552	0.8821	-0.0736	3.3840	1.8765	0.0606
FlET:WBV	1.4102	1.0287	-0.6060	3.4264	1.3709	0.1704
FlET:WlNi	-0.4010	0.5485	-1.4761	0.6740	-0.7311	0.4647
HtT:MasT	0.8386	1.3730	-1.8524	3.5297	0.6108	0.5413
HtT:McT	0.1106	1.2972	-2.4318	2.6530	0.0853	0.9320
HtT:MfT	1.0381	1.4620	-1.8273	3.9035	0.7100	0.4777
HtT:MiET	-0.4677	1.2354	-2.8891	1.9537	-0.3786	0.7050
HtT:Mnt	0.6114	1.4155	-2.1629	3.3856	0.4319	0.6658
HtT:PBT	1.8117	1.4730	-1.0753	4.6987	1.2300	0.2187
HtT:PlaSh	-0.6474	1.3329	-3.2599	1.9651	-0.4857	0.6272
HtT:Plt	0.9319	1.4483	-1.9068	3.7706	0.6434	0.5200
HtT:ReET	0.1213	1.3536	-2.5316	2.7743	0.0896	0.9286
HtT:rTMS	0.4986	1.4168	-2.2783	3.2756	0.3519	0.7249
HtT:tDCS	0.7358	1.4554	-2.1167	3.5882	0.5055	0.6132
HtT:WBV	0.4908	1.5403	-2.5283	3.5098	0.3186	0.7500
HtT:WlNi	-1.3205	1.2725	-3.8145	1.1736	-1.0377	0.2994
MasT:McT	-0.7280	0.6464	-1.9950	0.5390	-1.1262	0.2601
MasT:MfT	0.1995	0.8280	-1.4234	1.8223	0.2409	0.8096
MasT:MiET	-1.3063	0.6700	-2.6196	0.0070	-1.9496	0.0512
MasT:Mnt	-0.2272	0.7589	-1.7147	1.2602	-0.2994	0.7646
MasT:PBT	0.9731	0.8847	-0.7608	2.7070	1.1000	0.2713
MasT:PlaSh	-1.4860	0.5701	-2.6033	-0.3686	-2.6066	0.0091
MasT:Plt	0.0933	0.7264	-1.3304	1.5169	0.1284	0.8978
MasT:ReET	-0.7173	0.7428	-2.1732	0.7387	-0.9656	0.3343
MasT:rTMS	-0.3400	0.7454	-1.8010	1.1210	-0.4561	0.6483
MasT:tDCS	-0.1028	0.8163	-1.7028	1.4971	-0.1260	0.8997
MasT:WBV	-0.3478	1.0456	-2.3972	1.7015	-0.3327	0.7394
MasT:WlNi	-2.1591	0.5786	-3.2932	-1.0250	-3.7314	0.0002
McT:MfT	0.9274	0.8063	-0.6529	2.5078	1.1502	0.2501
McT:MiET	-0.5783	0.4640	-1.4877	0.3311	-1.2464	0.2126
McT:Mnt	0.5008	0.7134	-0.8975	1.8991	0.7019	0.4827
McT:PBT	1.7011	0.8217	0.0905	3.3117	2.0701	0.0384
McT:PlaSh	-0.7580	0.5381	-1.8126	0.2966	-1.4087	0.1589
McT:Plt	0.8213	0.8235	-0.7928	2.4353	0.9973	0.3186
McT:ReET	0.0107	0.6074	-1.1797	1.2011	0.0176	0.9859
McT:rTMS	0.3880	0.7213	-1.0257	1.8016	0.5379	0.5906
McT:tDCS	0.6251	0.7943	-0.9316	2.1819	0.7870	0.4313
McT:WBV	0.3801	0.9073	-1.3981	2.1584	0.4190	0.6752
McT:WlNi	-1.4311	0.3412	-2.0998	-0.7624	-4.1947	0.0000
MfT:MiET	-1.5058	0.8337	-3.1398	0.1282	-1.8061	0.0709
MfT:Mnt	-0.4267	0.9224	-2.2346	1.3813	-0.4626	0.6437
MfT:PBT	0.7737	0.9482	-1.0848	2.6321	0.8159	0.4145
MfT:PlaSh	-1.6854	0.6005	-2.8624	-0.5085	-2.8067	0.0050
MfT:Plt	-0.1062	1.0059	-2.0777	1.8654	-0.1056	0.9159
MfT:ReET	-0.9167	0.9057	-2.6919	0.8584	-1.0122	0.3115
MfT:rTMS	-0.5395	0.7690	-2.0466	0.9677	-0.7015	0.4830
MfT:tDCS	-0.3023	0.8378	-1.9444	1.3398	-0.3608	0.7182
MfT:WBV	-0.5473	1.1559	-2.8128	1.7182	-0.4735	0.6359
MfT:WlNi	-2.3585	0.7624	-3.8527	-0.8644	-3.0938	0.0020
MiET:Mnt	1.0791	0.7422	-0.3757	2.5339	1.4538	0.1460
MiET:PBT	2.2794	0.8485	0.6164	3.9424	2.6864	0.0072
MiET:PlaSh	-0.1797	0.5783	-1.3131	0.9538	-0.3107	0.7560
MiET:Plt	1.3996	0.8275	-0.2222	3.0214	1.6914	0.0908
MiET:ReET	0.5890	0.6393	-0.6640	1.8420	0.9214	0.3569
MiET:rTMS	0.9663	0.7517	-0.5071	2.4397	1.2854	0.1986
MiET:tDCS	1.2035	0.8221	-0.4077	2.8147	1.4640	0.1432
MiET:WBV	0.9585	0.9541	-0.9115	2.8284	1.0046	0.3151
MiET:WlNi	-0.8528	0.3982	-1.6332	-0.0723	-2.1416	0.0322
Mnt:PBT	1.2003	0.9557	-0.6727	3.0734	1.2560	0.2091
Mnt:PlaSh	-1.2588	0.7002	-2.6311	0.1136	-1.7977	0.0722
Mnt:Plt	0.3205	0.9481	-1.5378	2.1788	0.3380	0.7353
Mnt:ReET	-0.4900	0.8285	-2.1139	1.1338	-0.5915	0.5542
Mnt:rTMS	-0.1128	0.8491	-1.7770	1.5514	-0.1328	0.8943
Mnt:tDCS	0.1244	0.9119	-1.6630	1.9118	0.1364	0.8915
Mnt:WBV	-0.1206	1.0852	-2.2475	2.0062	-0.1112	0.9115
Mnt:WlNi	-1.9319	0.6455	-3.1969	-0.6668	-2.9930	0.0028
PBT:PlaSh	-2.4591	0.7338	-3.8973	-1.0209	-3.3513	0.0008
PBT:Plt	-0.8798	1.0412	-2.9205	1.1608	-0.8450	0.3981
PBT:ReET	-1.6904	0.9242	-3.5018	0.1210	-1.8290	0.0674
PBT:rTMS	-1.3131	0.8770	-3.0320	0.4058	-1.4973	0.1343
PBT:tDCS	-1.0760	0.9380	-2.9143	0.7624	-1.1471	0.2513
PBT:WBV	-1.3210	1.1627	-3.5999	0.9579	-1.1361	0.2559
PBT:WlNi	-3.1322	0.7706	-4.6425	-1.6218	-4.0646	0.0000
PlaSh:Plt	1.5793	0.8070	-0.0024	3.1609	1.9570	0.0503
PlaSh:ReET	0.7687	0.6780	-0.5601	2.0976	1.1338	0.2569
PlaSh:rTMS	1.1460	0.4803	0.2046	2.0874	2.3860	0.0170
PlaSh:tDCS	1.3831	0.5843	0.2380	2.5283	2.3674	0.0179
PlaSh:WBV	1.1381	0.9876	-0.7976	3.0739	1.1524	0.2492
PlaSh:WlNi	-0.6731	0.4696	-1.5936	0.2474	-1.4332	0.1518
Plt:ReET	-0.8106	0.8842	-2.5435	0.9224	-0.9167	0.3593
Plt:rTMS	-0.4333	0.9391	-2.2739	1.4073	-0.4614	0.6445
Plt:tDCS	-0.1961	0.9963	-2.1488	1.7565	-0.1969	0.8439
Plt:WBV	-0.4411	1.1637	-2.7219	1.8396	-0.3791	0.7046
Plt:WlNi	-2.2524	0.7719	-3.7652	-0.7396	-2.9181	0.0035
ReET:rTMS	0.3773	0.8309	-1.2512	2.0058	0.4541	0.6498
ReET:tDCS	0.6144	0.8950	-1.1398	2.3686	0.6865	0.4924
ReET:WBV	0.3694	1.0307	-1.6508	2.3896	0.3584	0.7200
ReET:WlNi	-1.4418	0.5569	-2.5334	-0.3502	-2.5888	0.0096
rTMS:tDCS	0.2372	0.7563	-1.2452	1.7195	0.3136	0.7539
rTMS:WBV	-0.0079	1.0982	-2.1604	2.1447	-0.0072	0.9943
rTMS:WlNi	-1.8191	0.6718	-3.1357	-0.5025	-2.7080	0.0068
tDCS:WBV	-0.2450	1.1475	-2.4941	2.0041	-0.2135	0.8309
tDCS:WlNi	-2.0562	0.7496	-3.5255	-0.5870	-2.7431	0.0061
WBV:WlNi	-1.8112	0.8759	-3.5279	-0.0945	-2.0679	0.0387

Code

# Forest plot of node-splitting
plot_netsplit_forest(split_sr)

Figure 3: Forest plot of node-splitting for Network

Code

# Heatmap
netheat(nma_sr)

Heatmap of contribution matrix for Network

Code

# Funnel plot
funnel(nma_sr,
    order = "WlNi",
)

Code

# Read data
data_lg <- read_excel("data/Banco de Dados_Rstudio (1).xlsx", sheet = "P | Lg") |>
    mutate(
        Mean1 = as.numeric(Mean1),
        Mean2 = as.numeric(Mean2),
        Mean3 = as.numeric(Mean3),
        SD1 = as.numeric(SD1),
        SD2 = as.numeric(SD2),
        SD3 = as.numeric(SD3),
    )


# Transform to contrast-based
pw <- pairwise(
    treat = list(Treat1, Treat2, Treat3),
    n = list(N1, N2, N3),
    mean = list(Mean1, Mean2, Mean3),
    sd = list(SD1, SD2, SD3),
    studlab = StudyID,
    data = data_lg,
    sm      = "MD"
)

# Check network connections
net_con <- netconnection(pw)
net_con

Number of studies: k = 29
Number of pairwise comparisons: m = 33
Number of treatments: n = 14
Number of designs: d = 17
Number of networks: 3

Details on subnetworks: 
 subnetwork  k  m n
          1 23 27 8
          2  1  1 2
          3  5  5 4

There are three sub-networks:

Subnet 1:
- 23 studies
- 27 comparisons
- 8 treatments

Subnet 2:
- 1 studies
- 1 comparisons
- 2 treatments

Subnet 3:
- 5 studies
- 5 comparisons
- 4 treatments

Tip

There are Three treatment sub-networks that do not connect.

Note

Please: Select the treatment sub-networks before proceeding.

Select the procedures performed

Important

the first subnet contains 23 studies, 27 comparisons and 8 treatments.

Code

# Filter data for subnet 1
sr1 <- tibble(
    treat1 = net_con$treat1,
    treat2 = net_con$treat2,
    subnet = net_con$subnet
) |>
    filter(subnet == 1)

# Display comparison table
sr1

Code

# Unique treatments in subnet 1
unique_treat1 <- sr1$treat1 |> unique()
unique_treat2 <- sr1$treat2 |> unique()

# Combination of all unique treatments
trat_sr1 <- unique(c(unique_treat1, unique_treat2))
print(trat_sr1)

[1] "AeET" "AqET" "ReET" "McT"  "CBT"  "MiET" "WlNi" "FlET"

Code

# Total number of treatments
length(trat_sr1)

[1] 8

Code

# Filter pairwise object only for comparisons within subnet 1
pw_plg_sr1 <- pw %>%
    filter(treat1 %in% trat_sr1 & treat2 %in% trat_sr1)

# Number of comparisons in subnet 1
nrow(pw_plg_sr1)

[1] 27

Code

# Fit NMA model for subnet 1
nma_sr1 <- netmeta(
    TE, seTE, treat1, treat2, studlab,
    data = pw_plg_sr1,
    n1 = n1,
    n2 = n2,
    random = TRUE,
    common = FALSE,
    sm = "MD", # can be "MD" or "SMD"
    ref = "WlNi" # Reference treatment (change if you want another control)
)

# Basic model summary
print(nma_sr1)

Number of studies: k = 23
Number of pairwise comparisons: m = 27
Number of observations: o = 1481
Number of treatments: n = 8
Number of designs: d = 13

Random effects model

Treatment estimate (other treatments vs 'WlNi'):
          MD             95% CI     z p-value
AeET -1.3392 [-2.1608; -0.5177] -3.19  0.0014
AqET -1.4499 [-2.3412; -0.5586] -3.19  0.0014
CBT  -1.2983 [-2.4184; -0.1783] -2.27  0.0231
FlET -0.2322 [-1.7516;  1.2873] -0.30  0.7646
McT  -1.1006 [-2.4584;  0.2571] -1.59  0.1121
MiET -1.3820 [-2.6824; -0.0816] -2.08  0.0373
ReET -2.1117 [-3.5656; -0.6579] -2.85  0.0044
WlNi       .                  .     .       .

Quantifying heterogeneity / inconsistency:
tau^2 = 0.8509; tau = 0.9224; I^2 = 73.4% [58.1%; 83.1%]

Tests of heterogeneity (within designs) and inconsistency (between designs):
                    Q d.f.  p-value
Total           67.60   18 < 0.0001
Within designs  56.21   10 < 0.0001
Between designs 11.40    8   0.1803

Details of network meta-analysis methods:
- Frequentist graph-theoretical approach
- DerSimonian-Laird estimator for tau^2
- Calculation of I^2 based on Q

Code

# Network plot
netgraph(
    nma_sr1,
    seq = "optimal",
    number.of.studies = TRUE,
    cex.points = n.trts,
    labels = paste0(trts, "\n(n=", round(n.trts), ")")
)

Network plot of treatments for Subnet 1 (P | Lg)

Code

# Detailed model summary
summary(nma_sr1)

Original data (with adjusted standard errors for multi-arm studies):

                       treat1 treat2      TE   seTE seTE.adj narms multiarm
Acosta-Gallego2018       AeET   AqET  0.5000 0.4574   1.0296     2         
Andrade2019              AqET   WlNi -1.0000 0.6137   1.1079     2         
Assis2006                AeET   AqET  0.0000 0.5601   1.0792     2         
Baptista2012             AeET   WlNi -2.9000 0.3860   0.9999     2         
Hakkinen2001             ReET   WlNi -3.9200 1.1181   1.4495     2         
Kayo2012                 AeET   ReET  0.3700 0.7979   1.5370     3        *
Kayo2012                 AeET   WlNi -1.3700 0.7203   1.4112     3        *
Kayo2012                 ReET   WlNi -1.7400 0.7288   1.4229     3        *
Larsson2015               McT   MiET  1.6100 0.4798   1.0397     2         
Letieri2013              AqET   WlNi -2.0800 0.3616   0.9908     2         
Mannerkorpi2000           McT   WlNi -0.5000 0.6372   1.1211     2         
Mengshoel1992            AeET   WlNi -0.6500 0.8641   1.2639     2         
Munguia-Izquierdo 2007   AqET   WlNi -0.9700 0.5334   1.0655     2         
Rooks2007                 CBT    McT  1.0000 0.5752   1.3721     3        *
Rooks2007                 CBT   MiET  0.9000 0.5016   1.2744     3        *
Rooks2007                 McT   MiET -0.1000 0.4732   1.2449     3        *
Sanudo2015               MiET   WlNi -0.3000 0.7371   1.1808     2         
Schachter2003            AeET   WlNi -0.0400 0.4272   1.0166     2         
Tomas-Carus2008          AqET   WlNi -1.3000 0.5888   1.0943     2         
Valim2003                AeET   FlET -1.1800 0.6042   1.1027     2         
Valkeinen2008            MiET   WlNi -1.7100 1.4134   1.6878     2         
Williams2010              CBT   WlNi -0.6000 0.2855   0.9656     2         
Hernando-Garijo2021      AeET   WlNi -1.5400 0.6724   1.1415     2         
Saranya2022               CBT   FlET -1.8700 0.9740   1.3414     2         
Erol2025                  CBT   WlNi -2.9000 0.5105   1.0543     2         
Fernandes2024            FlET   ReET  1.3000 0.5412   1.0695     2         
RodriguezDominguez2026    McT   MiET -0.7000 0.5822   1.0908     2         

Number of treatment arms per study (by decreasing number of arms):
                       narms multiarm
Kayo2012                   3        *
Rooks2007                  3        *
Acosta-Gallego2018         2         
Andrade2019                2         
Assis2006                  2         
Baptista2012               2         
Hakkinen2001               2         
Larsson2015                2         
Letieri2013                2         
Mannerkorpi2000            2         
Mengshoel1992              2         
Munguia-Izquierdo 2007     2         
Sanudo2015                 2         
Schachter2003              2         
Tomas-Carus2008            2         
Valim2003                  2         
Valkeinen2008              2         
Williams2010               2         
Hernando-Garijo2021        2         
Saranya2022                2         
Erol2025                   2         
Fernandes2024              2         
RodriguezDominguez2026     2         

Results (random effects model):

                       treat1 treat2      MD             95% CI
Acosta-Gallego2018       AeET   AqET  0.1106 [-0.8967;  1.1180]
Andrade2019              AqET   WlNi -1.4499 [-2.3412; -0.5586]
Assis2006                AeET   AqET  0.1106 [-0.8967;  1.1180]
Baptista2012             AeET   WlNi -1.3392 [-2.1608; -0.5177]
Hakkinen2001             ReET   WlNi -2.1117 [-3.5656; -0.6579]
Kayo2012                 AeET   ReET  0.7725 [-0.7290;  2.2741]
Kayo2012                 AeET   WlNi -1.3392 [-2.1608; -0.5177]
Kayo2012                 ReET   WlNi -2.1117 [-3.5656; -0.6579]
Larsson2015               McT   MiET  0.2813 [-0.8225;  1.3852]
Letieri2013              AqET   WlNi -1.4499 [-2.3412; -0.5586]
Mannerkorpi2000           McT   WlNi -1.1006 [-2.4584;  0.2571]
Mengshoel1992            AeET   WlNi -1.3392 [-2.1608; -0.5177]
Munguia-Izquierdo 2007   AqET   WlNi -1.4499 [-2.3412; -0.5586]
Rooks2007                 CBT    McT -0.1977 [-1.6574;  1.2620]
Rooks2007                 CBT   MiET  0.0837 [-1.3302;  1.4975]
Rooks2007                 McT   MiET  0.2813 [-0.8225;  1.3852]
Sanudo2015               MiET   WlNi -1.3820 [-2.6824; -0.0816]
Schachter2003            AeET   WlNi -1.3392 [-2.1608; -0.5177]
Tomas-Carus2008          AqET   WlNi -1.4499 [-2.3412; -0.5586]
Valim2003                AeET   FlET -1.1070 [-2.6079;  0.3938]
Valkeinen2008            MiET   WlNi -1.3820 [-2.6824; -0.0816]
Williams2010              CBT   WlNi -1.2983 [-2.4184; -0.1783]
Hernando-Garijo2021      AeET   WlNi -1.3392 [-2.1608; -0.5177]
Saranya2022               CBT   FlET -1.0662 [-2.7307;  0.5984]
Erol2025                  CBT   WlNi -1.2983 [-2.4184; -0.1783]
Fernandes2024            FlET   ReET  1.8796 [ 0.2967;  3.4624]
RodriguezDominguez2026    McT   MiET  0.2813 [-0.8225;  1.3852]

Number of studies: k = 23
Number of pairwise comparisons: m = 27
Number of observations: o = 1481
Number of treatments: n = 8
Number of designs: d = 13

Random effects model

Treatment estimate (other treatments vs 'WlNi'):
          MD             95% CI     z p-value
AeET -1.3392 [-2.1608; -0.5177] -3.19  0.0014
AqET -1.4499 [-2.3412; -0.5586] -3.19  0.0014
CBT  -1.2983 [-2.4184; -0.1783] -2.27  0.0231
FlET -0.2322 [-1.7516;  1.2873] -0.30  0.7646
McT  -1.1006 [-2.4584;  0.2571] -1.59  0.1121
MiET -1.3820 [-2.6824; -0.0816] -2.08  0.0373
ReET -2.1117 [-3.5656; -0.6579] -2.85  0.0044
WlNi       .                  .     .       .

Quantifying heterogeneity / inconsistency:
tau^2 = 0.8509; tau = 0.9224; I^2 = 73.4% [58.1%; 83.1%]

Tests of heterogeneity (within designs) and inconsistency (between designs):
                    Q d.f.  p-value
Total           67.60   18 < 0.0001
Within designs  56.21   10 < 0.0001
Between designs 11.40    8   0.1803

Details of network meta-analysis methods:
- Frequentist graph-theoretical approach
- DerSimonian-Laird estimator for tau^2
- Calculation of I^2 based on Q

Code

# Forest plot
forest(nma_sr1)

Code

# Pairwise comparison table with confidence intervals
render_league_table(nma_sr1)

Table 7: League table (random effects model) for Subnet 1

Comparison	AeET	AqET	CBT	FlET	McT	MiET	ReET	WlNi
AeET	AeET	0.2618 [-1.1983; 1.7218]	.	-1.1800 [-3.3412; 0.9812]	.	.	0.3700 [-2.0204; 2.7604]	-1.3512 [-2.3202; -0.3822]
AqET	0.1106 [-0.8967; 1.1180]	AqET	.	.	.	.	.	-1.3731 [-2.4135; -0.3328]
CBT	-0.0409 [-1.3840; 1.3022]	-0.1515 [-1.5681; 1.2650]	CBT	-1.8700 [-4.4992; 0.7592]	1.0000 [-1.1306; 3.1306]	0.9000 [-1.1579; 2.9579]	.	-1.6493 [-3.0449; -0.2536]
FlET	-1.1070 [-2.6079; 0.3938]	-1.2177 [-2.9080; 0.4726]	-1.0662 [-2.7307; 0.5984]	FlET	.	.	1.3000 [-0.7962; 3.3962]	.
McT	-0.2386 [-1.8102; 1.3331]	-0.3492 [-1.9683; 1.2699]	-0.1977 [-1.6574; 1.2620]	0.8685 [-1.0928; 2.8298]	McT	0.2997 [-0.8940; 1.4934]	.	-0.5000 [-2.6973; 1.6973]
MiET	0.0428 [-1.4800; 1.5655]	-0.0679 [-1.6394; 1.5036]	0.0837 [-1.3302; 1.4975]	1.1498 [-0.7739; 3.0736]	0.2813 [-0.8225; 1.3852]	MiET	.	-0.7633 [-2.6596; 1.1329]
ReET	0.7725 [-0.7290; 2.2741]	0.6619 [-0.9899; 2.3136]	0.8134 [-0.9299; 2.5567]	1.8796 [ 0.2967; 3.4624]	1.0111 [-0.9460; 2.9682]	0.7297 [-1.1887; 2.6482]	ReET	-2.6050 [-4.3945; -0.8155]
WlNi	-1.3392 [-2.1608; -0.5177]	-1.4499 [-2.3412; -0.5586]	-1.2983 [-2.4184; -0.1783]	-0.2322 [-1.7516; 1.2873]	-1.1006 [-2.4584; 0.2571]	-1.3820 [-2.6824; -0.0816]	-2.1117 [-3.5656; -0.6579]	WlNi

Code

# Treatment ranking
ranking_sr1 <- netrank(nma_sr1, small.values = "good")
print(ranking_sr1)

     P-score
ReET  0.8645
AqET  0.6432
MiET  0.6163
AeET  0.5879
CBT   0.5737
McT   0.4757
FlET  0.1713
WlNi  0.0674

Code

# Rankogram
ran_sr1 <- rankogram(nma_sr1)
plot(ran_sr1)

Code

# Design decomposition
decomp_sr1 <- decomp.design(nma_sr1)
print(decomp_sr1)

Q statistics to assess homogeneity / consistency

                    Q df  p-value
Total           67.60 18 < 0.0001
Within designs  56.21 10 < 0.0001
Between designs 11.40  8   0.1803

Design-specific decomposition of within-designs Q statistic

    Design     Q df  p-value
 WlNi:AeET 25.80  3 < 0.0001
  WlNi:CBT 15.46  1 < 0.0001
  McT:MiET  9.38  1   0.0022
 WlNi:AqET  4.31  3   0.2296
 WlNi:MiET  0.78  1   0.3764
 AeET:AqET  0.48  1   0.4893

Between-designs Q statistic after detaching of single designs
(influential designs have p-value markedly different from 0.1803)

 Detached design     Q df p-value
    CBT:McT:MiET  5.30  6  0.5062
       WlNi:ReET  7.60  7  0.3694
       WlNi:MiET  9.25  7  0.2351
        CBT:FlET  9.64  7  0.2100
        WlNi:CBT 10.03  7  0.1867
       FlET:ReET 10.07  7  0.1847
        McT:MiET 10.14  7  0.1810
        WlNi:McT 10.46  7  0.1638
       WlNi:AqET 11.09  7  0.1349
       AeET:AqET 11.09  7  0.1349
       WlNi:AeET 11.17  7  0.1315
       AeET:FlET 11.39  7  0.1223
  WlNi:AeET:ReET 11.27  6  0.0805

Q statistic to assess consistency under the assumption of
a full design-by-treatment interaction random effects model

                   Q df p-value tau.within tau2.within
Between designs 3.63  8  0.8886     1.1320      1.2814

Code

# Node-splitting analysis to detect inconsistency
split_sr1 <- netsplit(nma_sr1, show = "all")
render_netsplit_table(split_sr1)

Table 8: Node-splitting analysis for Subnet 1

comparison	TE	seTE	lower	upper	statistic	p
AeET:AqET	0.1106	0.5140	-0.8967	1.1180	0.2153	0.8296
AeET:CBT	-0.0409	0.6853	-1.3840	1.3022	-0.0597	0.9524
AeET:FlET	-1.1070	0.7658	-2.6079	0.3938	-1.4457	0.1483
AeET:McT	-0.2386	0.8019	-1.8102	1.3331	-0.2975	0.7661
AeET:MiET	0.0428	0.7769	-1.4800	1.5655	0.0551	0.9561
AeET:ReET	0.7725	0.7661	-0.7290	2.2741	1.0084	0.3133
AeET:WlNi	-1.3392	0.4192	-2.1608	-0.5177	-3.1949	0.0014
AqET:CBT	-0.1515	0.7227	-1.5681	1.2650	-0.2096	0.8339
AqET:FlET	-1.2177	0.8624	-2.9080	0.4726	-1.4120	0.1580
AqET:McT	-0.3492	0.8261	-1.9683	1.2699	-0.4227	0.6725
AqET:MiET	-0.0679	0.8018	-1.6394	1.5036	-0.0846	0.9325
AqET:ReET	0.6619	0.8427	-0.9899	2.3136	0.7854	0.4322
AqET:WlNi	-1.4499	0.4548	-2.3412	-0.5586	-3.1882	0.0014
CBT:FlET	-1.0662	0.8493	-2.7307	0.5984	-1.2554	0.2093
CBT:McT	-0.1977	0.7448	-1.6574	1.2620	-0.2654	0.7907
CBT:MiET	0.0837	0.7214	-1.3302	1.4975	0.1160	0.9077
CBT:ReET	0.8134	0.8895	-0.9299	2.5567	0.9145	0.3605
CBT:WlNi	-1.2983	0.5715	-2.4184	-0.1783	-2.2720	0.0231
FlET:McT	0.8685	1.0007	-1.0928	2.8298	0.8679	0.3855
FlET:MiET	1.1498	0.9815	-0.7739	3.0736	1.1715	0.2414
FlET:ReET	1.8796	0.8076	0.2967	3.4624	2.3274	0.0199
FlET:WlNi	-0.2322	0.7752	-1.7516	1.2873	-0.2995	0.7646
McT:MiET	0.2813	0.5632	-0.8225	1.3852	0.4995	0.6174
McT:ReET	1.0111	0.9985	-0.9460	2.9682	1.0126	0.3113
McT:WlNi	-1.1006	0.6927	-2.4584	0.2571	-1.5888	0.1121
MiET:ReET	0.7297	0.9788	-1.1887	2.6482	0.7455	0.4560
MiET:WlNi	-1.3820	0.6635	-2.6824	-0.0816	-2.0829	0.0373
ReET:WlNi	-2.1117	0.7418	-3.5656	-0.6579	-2.8469	0.0044

Code

# Forest plot of node-splitting
plot_netsplit_forest(split_sr1)

Figure 4: Forest plot of node-splitting for Subnet 1

Code

# Heatmap
netheat(nma_sr1)

Heatmap of contribution matrix for Subnet 1

Code

# Funnel plot
funnel(nma_sr1,
    order = "WlNi",
)

Select the procedures performed

Important

The second subnet contains 1 studies, 1 comparisons and 2 treatments. Due to its limited size, the analysis will be simplified.

Code

# Filter data for subnet 2
sr2 <- tibble(
    treat1 = net_con$treat1,
    treat2 = net_con$treat2,
    subnet = net_con$subnet
) |>
    filter(subnet == 2)

# Display first rows of comparison table
sr2 |> head()

Code

# Unique treatments in subnet 2
unique_treat1_sr2 <- sr2$treat1 |> unique()
print(unique_treat1_sr2)

[1] "DryN"

Code

unique_treat2_sr2 <- sr2$treat2 |> unique()
print(unique_treat2_sr2)

[1] "MasT"

Code

# Combination of all unique treatments
trat_sr2 <- unique(c(unique_treat1_sr2, unique_treat2_sr2))
print(trat_sr2)

[1] "DryN" "MasT"

Code

# Total number of treatments
length(trat_sr2)

[1] 2

Code

# Filter pairwise object only for comparisons within subnet 2
pw_plg_sr2 <- pw %>%
    filter(treat1 %in% trat_sr2 & treat2 %in% trat_sr2)

# Number of comparisons in subnet 2
nrow(pw_plg_sr2)

[1] 1

Code

# Number of unique studies
length(unique(pw_plg_sr2$studlab))

[1] 1

Code

# Fit NMA model for subnet 2
nma_sr2 <- netmeta(
    TE, seTE, treat1, treat2, studlab,
    data = pw_plg_sr2,
    n1 = N1,
    n2 = N2,
    random = TRUE,
    common = FALSE,
    sm = "MD",
    ref = "MasT" # Reference treatment (change if you want another control)
)

# Basic model summary
print(nma_sr2)

Number of studies: k = 1
Number of pairwise comparisons: m = 1
Number of observations: o = 59
Number of treatments: n = 2
Number of designs: d = 1

Random effects model

Treatment estimate ('DryN' vs 'MasT'):
          MD            95% CI     z p-value
DryN -1.0600 [-3.2020; 1.0820] -0.97  0.3321
MasT       .                 .     .       .

Quantifying heterogeneity:
tau^2 = NA; tau = NA

Details of network meta-analysis methods:
- Frequentist graph-theoretical approach
- DerSimonian-Laird estimator for tau^2

Code

# Network plot
netgraph(nma_sr2,
    seq = "optimal",
    number.of.studies = TRUE,
    cex.points = n.trts)

Network plot of treatments for Subnet 2 (P | Lg)

Code

# Detailed model summary
summary(nma_sr2)

Original data:

                   treat1 treat2      TE   seTE
Castro-Sanchez2011   DryN   MasT -1.0600 1.0929

Number of treatment arms per study:
                   narms
Castro-Sanchez2011     2

Results (random effects model):

                   treat1 treat2      MD            95% CI
Castro-Sanchez2011   DryN   MasT -1.0600 [-3.2020; 1.0820]

Number of studies: k = 1
Number of pairwise comparisons: m = 1
Number of observations: o = 59
Number of treatments: n = 2
Number of designs: d = 1

Random effects model

Treatment estimate ('DryN' vs 'MasT'):
          MD            95% CI     z p-value
DryN -1.0600 [-3.2020; 1.0820] -0.97  0.3321
MasT       .                 .     .       .

Quantifying heterogeneity:
tau^2 = NA; tau = NA

Details of network meta-analysis methods:
- Frequentist graph-theoretical approach
- DerSimonian-Laird estimator for tau^2

Select the procedures performed

Important

the third subnet contains 5 studies, 5 comparisons and 4 treatments.

Code

# Filter data for subnet 3
sr3 <- tibble(
    treat1 = net_con$treat1,
    treat2 = net_con$treat2,
    subnet = net_con$subnet
) |>
    filter(subnet == 3)

# Display first rows of comparison table
sr3 |> head()

Code

# Unique treatments in subnet 3
unique_treat1_sr3 <- sr3$treat1 |> unique()
print(unique_treat1_sr3)

[1] "MfT"  "Acu"  "rTMS"

Code

unique_treat2_sr3 <- sr3$treat2 |> unique()
print(unique_treat2_sr3)

[1] "PlaSh"

Code

# Combination of all unique treatments
trat_sr3 <- unique(c(unique_treat1_sr3, unique_treat2_sr3))
print(trat_sr3)

[1] "MfT"   "Acu"   "rTMS"  "PlaSh"

Code

# Total number of treatments
length(trat_sr3)

[1] 4

Code

# Filter pairwise object only for comparisons within subnet 3
pw_plg_sr3 <- pw %>%
    filter(treat1 %in% trat_sr3 & treat2 %in% trat_sr3)

# Number of comparisons in subnet 3
nrow(pw_plg_sr3)

[1] 5

Code

# Number of unique studies
length(unique(pw_plg_sr3$studlab))

[1] 5

Code

# Fit NMA model for subnet 3
nma_sr3 <- netmeta(
    TE, seTE, treat1, treat2, studlab,
    data = pw_plg_sr3,
    n1 = N1,
    n2 = N2,
    random = TRUE,
    common = FALSE,
    sm = "MD",
    ref = "PlaSh" # Reference treatment (change if you want another control)
)

# Basic model summary
print(nma_sr3)

Number of studies: k = 5
Number of pairwise comparisons: m = 5
Number of observations: o = 336
Number of treatments: n = 4
Number of designs: d = 3

Random effects model

Treatment estimate (other treatments vs 'PlaSh'):
           MD            95% CI     z p-value
Acu   -0.0800 [-1.9811; 1.8211] -0.08  0.9343
MfT   -1.1807 [-2.5901; 0.2286] -1.64  0.1006
PlaSh       .                 .     .       .
rTMS  -0.5561 [-1.7178; 0.6056] -0.94  0.3481

Quantifying heterogeneity / inconsistency:
tau^2 = 0.4444; tau = 0.6666; I^2 = 53.1% [0.0%; 86.5%]

Tests of heterogeneity (within designs) and inconsistency (between designs):
                   Q d.f. p-value
Total           4.26    2  0.1188
Within designs  4.26    2  0.1188
Between designs 0.00    0      --

Details of network meta-analysis methods:
- Frequentist graph-theoretical approach
- DerSimonian-Laird estimator for tau^2
- Calculation of I^2 based on Q

Code

# Network plot
netgraph(nma_sr3,
    seq = "optimal",
    number.of.studies = TRUE,
    cex.points = n.trts)

Network plot of treatments for Subnet 3 (P | Lg)

Code

# Detailed model summary
summary(nma_sr3)

Original data:

            treat1 treat2      TE   seTE
Alfano2001     MfT  PlaSh -0.8500 0.5572
Colbert1999    MfT  PlaSh -1.9000 1.0942
Harris2005     Acu  PlaSh -0.0800 0.7046
Mhalla2011   PlaSh   rTMS  1.1700 0.4383
Silva2025    PlaSh   rTMS -0.2000 0.5827

Number of treatment arms per study:
            narms
Alfano2001      2
Colbert1999     2
Harris2005      2
Mhalla2011      2
Silva2025       2

Results (random effects model):

            treat1 treat2      MD            95% CI
Alfano2001     MfT  PlaSh -1.1807 [-2.5901; 0.2286]
Colbert1999    MfT  PlaSh -1.1807 [-2.5901; 0.2286]
Harris2005     Acu  PlaSh -0.0800 [-1.9811; 1.8211]
Mhalla2011   PlaSh   rTMS  0.5561 [-0.6056; 1.7178]
Silva2025    PlaSh   rTMS  0.5561 [-0.6056; 1.7178]

Number of studies: k = 5
Number of pairwise comparisons: m = 5
Number of observations: o = 336
Number of treatments: n = 4
Number of designs: d = 3

Random effects model

Treatment estimate (other treatments vs 'PlaSh'):
           MD            95% CI     z p-value
Acu   -0.0800 [-1.9811; 1.8211] -0.08  0.9343
MfT   -1.1807 [-2.5901; 0.2286] -1.64  0.1006
PlaSh       .                 .     .       .
rTMS  -0.5561 [-1.7178; 0.6056] -0.94  0.3481

Quantifying heterogeneity / inconsistency:
tau^2 = 0.4444; tau = 0.6666; I^2 = 53.1% [0.0%; 86.5%]

Tests of heterogeneity (within designs) and inconsistency (between designs):
                   Q d.f. p-value
Total           4.26    2  0.1188
Within designs  4.26    2  0.1188
Between designs 0.00    0      --

Details of network meta-analysis methods:
- Frequentist graph-theoretical approach
- DerSimonian-Laird estimator for tau^2
- Calculation of I^2 based on Q

Code

# Forest plot
forest(nma_sr3)

Forest plot of treatment effects for Subnet 3

Code

# Pairwise comparison table with confidence intervals
render_league_table(nma_sr3)

Table 9: League table (random effects model) for Subnet 3

Comparison	Acu	MfT	PlaSh	rTMS
Acu	Acu	.	-0.0800 [-1.9811; 1.8211]	.
MfT	1.1007 [-1.2658; 3.4673]	MfT	-1.1807 [-2.5901; 0.2286]	.
PlaSh	-0.0800 [-1.9811; 1.8211]	-1.1807 [-2.5901; 0.2286]	PlaSh	0.5561 [-0.6056; 1.7178]
rTMS	0.4761 [-1.7519; 2.7041]	-0.6246 [-2.4511; 1.2018]	0.5561 [-0.6056; 1.7178]	rTMS

Code

# Treatment ranking
ranking_sr3 <- netrank(nma_sr3, small.values = "good")
print(ranking_sr3)

      P-score
MfT    0.8391
rTMS   0.5799
Acu    0.3505
PlaSh  0.2305

Code

# Rankogram
ran_sr3 <- rankogram(nma_sr3)
plot(ran_sr3)

Code

# Design decomposition
decomp_sr3 <- decomp.design(nma_sr3)
print(decomp_sr3)

Q statistics to assess homogeneity / consistency

                   Q df p-value
Total           4.26  2  0.1188
Within designs  4.26  2  0.1188
Between designs 0.00  0      --

Design-specific decomposition of within-designs Q statistic

     Design    Q df p-value
 PlaSh:rTMS 3.53  1  0.0603
  PlaSh:MfT 0.73  1  0.3925

Q statistic to assess consistency under the assumption of
a full design-by-treatment interaction random effects model

                   Q df p-value tau.within tau2.within
Between designs 0.00  0      --     0.6666      0.4444

Code

# Node-splitting analysis to detect inconsistency
split_sr3 <- netsplit(nma_sr3, show = "all")
render_netsplit_table(split_sr3)

Table 10: Node-splitting analysis for Subnet 3

comparison	TE	seTE	lower	upper	statistic	p
Acu:MfT	1.1007	1.2075	-1.2658	3.4673	0.9116	0.3620
Acu:PlaSh	-0.0800	0.9700	-1.9811	1.8211	-0.0825	0.9343
Acu:rTMS	0.4761	1.1367	-1.7519	2.7041	0.4188	0.6753
MfT:PlaSh	-1.1807	0.7191	-2.5901	0.2286	-1.6420	0.1006
MfT:rTMS	-0.6246	0.9319	-2.4511	1.2018	-0.6703	0.5027
rTMS:PlaSh	-0.5561	0.5927	-1.7178	0.6056	-0.9382	0.3481

Code

# Forest plot of node-splitting
plot_netsplit_forest(split_sr3)

Figure 5: Forest plot of node-splitting for Subnet 3

Code

# Heatmap
netheat(nma_sr3)

Code

# Funnel plot
funnel(nma_sr3,
    order = "PlaSh",
)

Code

# Read data
data_qim <- read_excel("data/Banco de Dados_Rstudio (1).xlsx", sheet = "Q | Im") |>
    mutate(
        Mean1 = as.numeric(Mean1),
        Mean2 = as.numeric(Mean2),
        SD1 = as.numeric(SD1),
        SD2 = as.numeric(SD2)
    )


# Transform to contrast-based
pw <- pairwise(
    treat = list(Treat1, Treat2),
    n = list(N1, N2),
    mean = list(Mean1, Mean2),
    sd = list(SD1, SD2),
    studlab = StudyID,
    data = data_qim
)

# Check network connections
net_con <- netconnection(pw)
net_con

Number of studies: k = 20
Number of pairwise comparisons: m = 20
Number of treatments: n = 11
Number of designs: d = 10
Number of networks: 1

There are network:

Network:
- 20 studies
- 20 comparisons
- 11 treatments

Tip

The network is fully connected.

1.0.2 Network

Select the procedures performed

Important

The network contain 20 studies, 20 comparisons and 11 treatments.

Code

# Filter data for network
sr <- tibble(
    treat1 = net_con$treat1,
    treat2 = net_con$treat2,
    subnet = net_con$subnet
)


# Display comparison table
sr

Code

# Unique treatments in network
unique_treat_sr1 <- sr$treat1 |> unique()
print(unique_treat_sr1)

[1] "Mnt"  "PbT"  "CBT"  "rTMS" "tDCS" "HtT"  "Bal"  "McT"  "Cry"

Code

unique_treat_sr2 <- sr$treat2 |> unique()
print(unique_treat_sr2)

[1] "WlNi"  "PlaSh"

Code

# Combination of all unique treatments
trat_sr <- unique(c(unique_treat_sr1, unique_treat_sr2))
print(trat_sr)

 [1] "Mnt"   "PbT"   "CBT"   "rTMS"  "tDCS"  "HtT"   "Bal"   "McT"   "Cry"  
[10] "WlNi"  "PlaSh"

Code

# Total number of treatments
length(trat_sr)

[1] 11

Code

# Filter pairwise object only for comparisons within network
pw_qim <- pw %>%
    filter(treat1 %in% trat_sr & treat2 %in% trat_sr)

# Number of comparisons in network
nrow(pw_qim)

[1] 20

Code

# Fit NMA model for network
nma_sr <- netmeta(
    TE, seTE, treat1, treat2, studlab,
    data = pw_qim,
    n1 = N1,
    n2 = N2,
    random = TRUE,
    common = FALSE,
    sm = "MD",
    ref = "WlNi" # Reference treatment (change if you want another control)
)

# Basic model summary
print(nma_sr)

Number of studies: k = 20
Number of pairwise comparisons: m = 20
Number of observations: o = 886
Number of treatments: n = 11
Number of designs: d = 10

Random effects model

Treatment estimate (other treatments vs 'WlNi'):
            MD               95% CI     z  p-value
Bal   -15.0330 [-25.0820;  -4.9840] -2.93   0.0034
CBT    -1.1924 [-25.0968;  22.7120] -0.10   0.9221
Cry   -30.1200 [-42.4084; -17.8316] -4.80 < 0.0001
HtT   -24.1300 [-35.8403; -12.4197] -4.04 < 0.0001
McT    -7.1969 [-15.4843;   1.0906] -1.70   0.0887
Mnt     1.6600 [-13.9760;  17.2960]  0.21   0.8352
PbT    -3.0576 [-23.6075;  17.4923] -0.29   0.7706
PlaSh   8.4076 [-10.1330;  26.9483]  0.89   0.3741
rTMS   -0.1422 [-19.7652;  19.4808] -0.01   0.9887
tDCS   -5.7600 [-21.9299;  10.4099] -0.70   0.4851
WlNi         .                    .     .        .

Quantifying heterogeneity / inconsistency:
tau^2 = 30.3434; tau = 5.5085; I^2 = 63% [29.0%; 80.7%]

Tests of heterogeneity (within designs) and inconsistency (between designs):
                 Q d.f. p-value
Total           27   10  0.0026
Within designs  27   10  0.0026
Between designs  0    0      --

Details of network meta-analysis methods:
- Frequentist graph-theoretical approach
- DerSimonian-Laird estimator for tau^2
- Calculation of I^2 based on Q

Code

# Network plot
netgraph(nma_sr,
    seq = "optimal",
    number.of.studies = TRUE,
    cex.points = n.trts,
    labels = paste0(trts, "\n(n=", round(n.trts), ")")
)

Network plot of treatments for Network (Q | Im)

Code

# Detailed model summary
summary(nma_sr)

Original data:

               treat1 treat2       TE    seTE
Albers2018        Mnt   WlNi   1.6600  5.7706
Armagam2006       PbT  PlaSh  -5.1300  3.5183
Babu2007          CBT  PlaSh  -9.6000  5.3779
Boyer2014       PlaSh   rTMS   1.0000  4.7100
Curatolo2017    PlaSh   tDCS  28.2800  8.5783
Fagerlund2015    tDCS   WlNi  -5.7600  6.1417
Fioravanti2009    HtT   WlNi -24.1300  2.3140
Fioravanti2007    Bal   WlNi -21.3500  4.2760
Gur2002a          PbT  PlaSh -17.2800  2.9780
Hamnes2012        McT   WlNi  -2.7600  2.4785
Lee2012         PlaSh   rTMS   1.4000 16.3233
Mhalla2011      PlaSh   rTMS   9.3000  3.8196
Ozkurt2011        Bal   WlNi  -7.6000  5.1841
Passard2007     PlaSh   rTMS  10.4000  2.7307
Salaffi2015       McT   WlNi -11.4600  2.1713
Short2011       PlaSh   rTMS  11.7600  9.8838
Valle2009       PlaSh   tDCS  15.3100 11.9744
Yagci2014       PlaSh   rTMS  14.0300  6.3821
Loreti2023      PlaSh   tDCS   9.6200  1.2652
Kiyak2022         Cry   WlNi -30.1200  2.9943

Number of treatment arms per study:
               narms
Albers2018         2
Armagam2006        2
Babu2007           2
Boyer2014          2
Curatolo2017       2
Fagerlund2015      2
Fioravanti2009     2
Fioravanti2007     2
Gur2002a           2
Hamnes2012         2
Lee2012            2
Mhalla2011         2
Ozkurt2011         2
Passard2007        2
Salaffi2015        2
Short2011          2
Valle2009          2
Yagci2014          2
Loreti2023         2
Kiyak2022          2

Results (random effects model):

               treat1 treat2       MD               95% CI
Albers2018        Mnt   WlNi   1.6600 [-13.9760;  17.2960]
Armagam2006       PbT  PlaSh -11.4652 [-20.3277;  -2.6028]
Babu2007          CBT  PlaSh  -9.6000 [-24.6886;   5.4886]
Boyer2014       PlaSh   rTMS   8.5498 [  2.1228;  14.9768]
Curatolo2017    PlaSh   tDCS  14.1676 [  5.0963;  23.2389]
Fagerlund2015    tDCS   WlNi  -5.7600 [-21.9299;  10.4099]
Fioravanti2009    HtT   WlNi -24.1300 [-35.8403; -12.4197]
Fioravanti2007    Bal   WlNi -15.0330 [-25.0820;  -4.9840]
Gur2002a          PbT  PlaSh -11.4652 [-20.3277;  -2.6028]
Hamnes2012        McT   WlNi  -7.1969 [-15.4843;   1.0906]
Lee2012         PlaSh   rTMS   8.5498 [  2.1228;  14.9768]
Mhalla2011      PlaSh   rTMS   8.5498 [  2.1228;  14.9768]
Ozkurt2011        Bal   WlNi -15.0330 [-25.0820;  -4.9840]
Passard2007     PlaSh   rTMS   8.5498 [  2.1228;  14.9768]
Salaffi2015       McT   WlNi  -7.1969 [-15.4843;   1.0906]
Short2011       PlaSh   rTMS   8.5498 [  2.1228;  14.9768]
Valle2009       PlaSh   tDCS  14.1676 [  5.0963;  23.2389]
Yagci2014       PlaSh   rTMS   8.5498 [  2.1228;  14.9768]
Loreti2023      PlaSh   tDCS  14.1676 [  5.0963;  23.2389]
Kiyak2022         Cry   WlNi -30.1200 [-42.4084; -17.8316]

Number of studies: k = 20
Number of pairwise comparisons: m = 20
Number of observations: o = 886
Number of treatments: n = 11
Number of designs: d = 10

Random effects model

Treatment estimate (other treatments vs 'WlNi'):
            MD               95% CI     z  p-value
Bal   -15.0330 [-25.0820;  -4.9840] -2.93   0.0034
CBT    -1.1924 [-25.0968;  22.7120] -0.10   0.9221
Cry   -30.1200 [-42.4084; -17.8316] -4.80 < 0.0001
HtT   -24.1300 [-35.8403; -12.4197] -4.04 < 0.0001
McT    -7.1969 [-15.4843;   1.0906] -1.70   0.0887
Mnt     1.6600 [-13.9760;  17.2960]  0.21   0.8352
PbT    -3.0576 [-23.6075;  17.4923] -0.29   0.7706
PlaSh   8.4076 [-10.1330;  26.9483]  0.89   0.3741
rTMS   -0.1422 [-19.7652;  19.4808] -0.01   0.9887
tDCS   -5.7600 [-21.9299;  10.4099] -0.70   0.4851
WlNi         .                    .     .        .

Quantifying heterogeneity / inconsistency:
tau^2 = 30.3434; tau = 5.5085; I^2 = 63% [29.0%; 80.7%]

Tests of heterogeneity (within designs) and inconsistency (between designs):
                 Q d.f. p-value
Total           27   10  0.0026
Within designs  27   10  0.0026
Between designs  0    0      --

Details of network meta-analysis methods:
- Frequentist graph-theoretical approach
- DerSimonian-Laird estimator for tau^2
- Calculation of I^2 based on Q

Code

# Forest plot
forest(nma_sr)

Code

# Pairwise comparison table with confidence intervals
render_league_table(nma_sr)

Table 11: League table (random effects model) for Network

Comparison	Bal	CBT	Cry	HtT	McT	Mnt	PbT	PlaSh	rTMS	tDCS	WlNi
Bal	Bal	.	.	.	.	.	.	.	.	.	-15.0330 [-25.0820; -4.9840]
CBT	-13.8406 [-39.7713; 12.0901]	CBT	.	.	.	.	.	-9.6000 [-24.6886; 5.4886]	.	.	.
Cry	15.0870 [ -0.7871; 30.9611]	28.9276 [ 2.0496; 55.8056]	Cry	.	.	.	.	.	.	.	-30.1200 [-42.4084; -17.8316]
HtT	9.0970 [ -6.3339; 24.5279]	22.9376 [ -3.6810; 49.5563]	-5.9900 [-22.9646; 10.9846]	HtT	.	.	.	.	.	.	-24.1300 [-35.8403; -12.4197]
McT	-7.8361 [-20.8616; 5.1894]	6.0045 [-19.2957; 31.3047]	-22.9231 [-37.7450; -8.1013]	-16.9331 [-31.2793; -2.5869]	McT	.	.	.	.	.	-7.1969 [-15.4843; 1.0906]
Mnt	-16.6930 [-35.2797; 1.8937]	-2.8524 [-31.4164; 25.7117]	-31.7800 [-51.6669; -11.8931]	-25.7900 [-45.3250; -6.2550]	-8.8569 [-26.5534; 8.8396]	Mnt	.	.	.	.	1.6600 [-13.9760; 17.2960]
PbT	-11.9754 [-34.8507; 10.8999]	1.8652 [-15.6335; 19.3640]	-27.0624 [-51.0061; -3.1186]	-21.0724 [-44.7247; 2.5799]	-4.1393 [-26.2973; 18.0188]	4.7176 [-21.1045; 30.5397]	PbT	-11.4652 [-20.3277; -2.6028]	.	.	.
PlaSh	-23.4406 [-44.5294; -2.3518]	-9.6000 [-24.6886; 5.4886]	-38.5276 [-60.7709; -16.2844]	-32.5376 [-54.4668; -10.6085]	-15.6045 [-35.9130; 4.7041]	-6.7476 [-31.0013; 17.5060]	-11.4652 [-20.3277; -2.6028]	PlaSh	8.5498 [ 2.1228; 14.9768]	14.1676 [ 5.0963; 23.2389]	.
rTMS	-14.8908 [-36.9372; 7.1556]	-1.0502 [-17.4506; 15.3502]	-29.9778 [-53.1310; -6.8247]	-23.9878 [-46.8394; -1.1363]	-7.0547 [-28.3560; 14.2466]	1.8022 [-23.2886; 26.8929]	-2.9154 [-13.8630; 8.0321]	8.5498 [ 2.1228; 14.9768]	rTMS	.	.
tDCS	-9.2730 [-28.3111; 9.7651]	4.5676 [-13.0379; 22.1731]	-24.3600 [-44.6694; -4.0506]	-18.3700 [-38.3349; 1.5949]	-1.4369 [-19.6069; 16.7331]	7.4200 [-15.0734; 29.9134]	2.7024 [ -9.9795; 15.3843]	14.1676 [ 5.0963; 23.2389]	5.6178 [ -5.4995; 16.7352]	tDCS	-5.7600 [-21.9299; 10.4099]
WlNi	-15.0330 [-25.0820; -4.9840]	-1.1924 [-25.0968; 22.7120]	-30.1200 [-42.4084; -17.8316]	-24.1300 [-35.8403; -12.4197]	-7.1969 [-15.4843; 1.0906]	1.6600 [-13.9760; 17.2960]	-3.0576 [-23.6075; 17.4923]	8.4076 [-10.1330; 26.9483]	-0.1422 [-19.7652; 19.4808]	-5.7600 [-21.9299; 10.4099]	WlNi

Code

# Treatment ranking
ranking_sr <- netrank(nma_sr, small.values = "good")
print(ranking_sr)

      P-score
Cry    0.9676
HtT    0.8962
Bal    0.7418
McT    0.5482
tDCS   0.5346
PbT    0.4432
CBT    0.3815
rTMS   0.3345
WlNi   0.3025
Mnt    0.2819
PlaSh  0.0680

Code

# Rankogram
ran_sr <- rankogram(nma_sr)
plot(ran_sr)

Code

# Design decomposition
decomp_sr <- decomp.design(nma_sr)
print(decomp_sr)

Q statistics to assess homogeneity / consistency

                    Q df p-value
Total           27.00 10  0.0026
Within designs  27.00 10  0.0026
Between designs  0.00  0      --

Design-specific decomposition of within-designs Q statistic

     Design    Q df p-value
   WlNi:McT 6.97  1  0.0083
  PbT:PlaSh 6.95  1  0.0084
   WlNi:Bal 4.19  1  0.0407
 PlaSh:tDCS 4.82  2  0.0896
 PlaSh:rTMS 4.07  5  0.5398

Q statistic to assess consistency under the assumption of
a full design-by-treatment interaction random effects model

                   Q df p-value tau.within tau2.within
Between designs 0.00  0      --     5.5085     30.3434

Code

# Node-splitting analysis to detect inconsistency
split_sr <- netsplit(nma_sr, show = "all")
render_netsplit_table(split_sr)

Table 12: Node-splitting analysis for Network

comparison	TE	seTE	lower	upper	statistic	p
Bal:CBT	-13.8406	13.2302	-39.7713	12.0901	-1.0461	0.2955
Bal:Cry	15.0870	8.0992	-0.7871	30.9611	1.8628	0.0625
Bal:HtT	9.0970	7.8731	-6.3339	24.5279	1.1555	0.2479
Bal:McT	-7.8361	6.6458	-20.8616	5.1894	-1.1791	0.2384
Bal:Mnt	-16.6930	9.4832	-35.2797	1.8937	-1.7603	0.0784
Bal:PbT	-11.9754	11.6713	-34.8507	10.8999	-1.0261	0.3049
Bal:PlaSh	-23.4406	10.7598	-44.5294	-2.3518	-2.1785	0.0294
Bal:rTMS	-14.8908	11.2484	-36.9372	7.1556	-1.3238	0.1856
Bal:tDCS	-9.2730	9.7135	-28.3111	9.7651	-0.9547	0.3398
Bal:WlNi	-15.0330	5.1271	-25.0820	-4.9840	-2.9321	0.0034
CBT:Cry	28.9276	13.7135	2.0496	55.8056	2.1094	0.0349
CBT:HtT	22.9376	13.5812	-3.6810	49.5563	1.6889	0.0912
CBT:McT	6.0045	12.9085	-19.2957	31.3047	0.4652	0.6418
CBT:Mnt	-2.8524	14.5738	-31.4164	25.7117	-0.1957	0.8448
CBT:PbT	1.8652	8.9281	-15.6335	19.3640	0.2089	0.8345
CBT:PlaSh	-9.6000	7.6984	-24.6886	5.4886	-1.2470	0.2124
CBT:rTMS	-1.0502	8.3677	-17.4506	15.3502	-0.1255	0.9001
CBT:tDCS	4.5676	8.9826	-13.0379	22.1731	0.5085	0.6111
CBT:WlNi	-1.1924	12.1963	-25.0968	22.7120	-0.0978	0.9221
Cry:HtT	-5.9900	8.6607	-22.9646	10.9846	-0.6916	0.4892
Cry:McT	-22.9231	7.5623	-37.7450	-8.1013	-3.0312	0.0024
Cry:Mnt	-31.7800	10.1466	-51.6669	-11.8931	-3.1321	0.0017
Cry:PbT	-27.0624	12.2164	-51.0061	-3.1186	-2.2152	0.0267
Cry:PlaSh	-38.5276	11.3488	-60.7709	-16.2844	-3.3949	0.0007
Cry:rTMS	-29.9778	11.8130	-53.1310	-6.8247	-2.5377	0.0112
Cry:tDCS	-24.3600	10.3621	-44.6694	-4.0506	-2.3509	0.0187
Cry:WlNi	-30.1200	6.2697	-42.4084	-17.8316	-4.8040	0.0000
HtT:McT	-16.9331	7.3196	-31.2793	-2.5869	-2.3134	0.0207
HtT:Mnt	-25.7900	9.9670	-45.3250	-6.2550	-2.5875	0.0097
HtT:PbT	-21.0724	12.0677	-44.7247	2.5799	-1.7462	0.0808
HtT:PlaSh	-32.5376	11.1885	-54.4668	-10.6085	-2.9081	0.0036
HtT:rTMS	-23.9878	11.6592	-46.8394	-1.1363	-2.0574	0.0396
HtT:tDCS	-18.3700	10.1864	-38.3349	1.5949	-1.8034	0.0713
HtT:WlNi	-24.1300	5.9748	-35.8403	-12.4197	-4.0387	0.0001
McT:Mnt	-8.8569	9.0290	-26.5534	8.8396	-0.9809	0.3266
McT:PbT	-4.1393	11.3053	-26.2973	18.0188	-0.3661	0.7143
McT:PlaSh	-15.6045	10.3617	-35.9130	4.7041	-1.5060	0.1321
McT:rTMS	-7.0547	10.8682	-28.3560	14.2466	-0.6491	0.5163
McT:tDCS	-1.4369	9.2706	-19.6069	16.7331	-0.1550	0.8768
McT:WlNi	-7.1969	4.2284	-15.4843	1.0906	-1.7020	0.0887
Mnt:PbT	4.7176	13.1748	-21.1045	30.5397	0.3581	0.7203
Mnt:PlaSh	-6.7476	12.3745	-31.0013	17.5060	-0.5453	0.5856
Mnt:rTMS	1.8022	12.8017	-23.2886	26.8929	0.1408	0.8880
Mnt:tDCS	7.4200	11.4764	-15.0734	29.9134	0.6465	0.5179
Mnt:WlNi	1.6600	7.9777	-13.9760	17.2960	0.2081	0.8352
PbT:PlaSh	-11.4652	4.5217	-20.3277	-2.6028	-2.5356	0.0112
PbT:rTMS	-2.9154	5.5856	-13.8630	8.0321	-0.5220	0.6017
PbT:tDCS	2.7024	6.4705	-9.9795	15.3843	0.4176	0.6762
PbT:WlNi	-3.0576	10.4848	-23.6075	17.4923	-0.2916	0.7706
PlaSh:rTMS	8.5498	3.2792	2.1228	14.9768	2.6073	0.0091
PlaSh:tDCS	14.1676	4.6283	5.0963	23.2389	3.0611	0.0022
PlaSh:WlNi	8.4076	9.4597	-10.1330	26.9483	0.8888	0.3741
rTMS:tDCS	5.6178	5.6722	-5.4995	16.7352	0.9904	0.3220
rTMS:WlNi	-0.1422	10.0119	-19.7652	19.4808	-0.0142	0.9887
tDCS:WlNi	-5.7600	8.2501	-21.9299	10.4099	-0.6982	0.4851

Code

# Forest plot of node-splitting
plot_netsplit_forest(split_sr)

Figure 6: Forest plot of node-splitting for Network

Code

# Heatmap
netheat(nma_sr)

Code

# Funnel plot
funnel(nma_sr,
    order = "WlNi",
)

Code

# Read data
data_qsh <- read_excel("data/Banco de Dados_Rstudio (1).xlsx", sheet = "Q | Sh") |>
    mutate(
        Mean1 = as.numeric(Mean1),
        Mean2 = as.numeric(Mean2),
        Mean3 = as.numeric(Mean3),
        SD1 = as.numeric(SD1),
        SD2 = as.numeric(SD2),
        SD3 = as.numeric(SD3),
    )


# Transform to contrast-based
pw <- pairwise(
    treat = list(Treat1, Treat2, Treat3),
    n = list(N1, N2, N3),
    mean = list(Mean1, Mean2, Mean3),
    sd = list(SD1, SD2, SD3),
    studlab = StudyID,
    data = data_qsh,
    sm      = "MD"
)

# Check network connections
net_con <- netconnection(pw)
net_con

Number of studies: k = 118
Number of pairwise comparisons: m = 138
Number of treatments: n = 21
Number of designs: d = 52
Number of networks: 1

There are network:

Network:
- 118 studies
- 138 comparisons
- 22 treatments

Tip

The network is fully connected.

1.0.3 Network

Select the procedures performed

Important

The network contain 118 studies, 138 comparisons and 22 treatments.

Code

# Filter data for network
sr <- tibble(
    treat1 = net_con$treat1,
    treat2 = net_con$treat2,
    subnet = net_con$subnet
)


# Display comparison table
sr

Code

# Unique treatments in network
unique_treat_sr1 <- sr$treat1 |> unique()
print(unique_treat_sr1)

 [1] "Mnt"   "McT"   "WBV"   "MfT"   "AqET"  "Bal"   "AeET"  "FlET"  "ReET" 
[10] "CBT"   "rTMS"  "DryN"  "MiET"  "Elec"  "MasT"  "tDCS"  "Acu"   "PbT"  
[19] "PlaSh" "WlNi"

Code

unique_treat_sr2 <- sr$treat2 |> unique()
print(unique_treat_sr2)

 [1] "WlNi"  "WBV"   "PlaSh" "Bal"   "AqET"  "ReET"  "McT"   "FlET"  "MasT" 
[10] "Plt"   "CBT"   "MiET"  "Mnt"   "AeET"  "PbT"

Code

# Combination of all unique treatments
trat_sr <- unique(c(unique_treat_sr1, unique_treat_sr2))
print(trat_sr)

 [1] "Mnt"   "McT"   "WBV"   "MfT"   "AqET"  "Bal"   "AeET"  "FlET"  "ReET" 
[10] "CBT"   "rTMS"  "DryN"  "MiET"  "Elec"  "MasT"  "tDCS"  "Acu"   "PbT"  
[19] "PlaSh" "WlNi"  "Plt"

Code

# Total number of treatments
length(trat_sr)

[1] 21

Code

# Filter pairwise object only for comparisons within network
pw_qsh <- pw %>%
    filter(treat1 %in% trat_sr & treat2 %in% trat_sr)

# Number of comparisons in network
nrow(pw_qsh)

[1] 138

Code

# Fit NMA model for network
nma_sr <- netmeta(
    TE, seTE, treat1, treat2, studlab,
    data = pw_qsh,
    n1 = N1,
    n2 = N2,
    random = TRUE,
    common = FALSE,
    sm = "MD",
    ref = "WlNi" # Reference treatment (change if you want another control)
)

# Basic model summary
print(nma_sr)

Number of studies: k = 118
Number of pairwise comparisons: m = 138
Number of observations: o = 8059
Number of treatments: n = 21
Number of designs: d = 52

Random effects model

Treatment estimate (other treatments vs 'WlNi'):
            MD               95% CI     z  p-value
Acu   -22.5548 [-36.8555;  -8.2541] -3.09   0.0020
AeET  -13.8785 [-21.6321;  -6.1249] -3.51   0.0005
AqET  -15.2378 [-24.3866;  -6.0889] -3.26   0.0011
Bal   -16.5359 [-30.3793;  -2.6924] -2.34   0.0192
CBT    -9.5513 [-14.4435;  -4.6591] -3.83   0.0001
DryN  -24.8737 [-43.8033;  -5.9441] -2.58   0.0100
Elec  -19.7168 [-39.3637;  -0.0699] -1.97   0.0492
FlET   -7.3847 [-17.5890;   2.8195] -1.42   0.1561
MasT  -16.9246 [-28.5617;  -5.2875] -2.85   0.0044
McT   -13.2800 [-18.9144;  -7.6456] -4.62 < 0.0001
MfT   -22.2476 [-39.9999;  -4.4952] -2.46   0.0140
MiET   -9.4771 [-15.8232;  -3.1311] -2.93   0.0034
Mnt   -11.4771 [-25.5926;   2.6384] -1.59   0.1110
PbT   -19.1555 [-36.1579;  -2.1531] -2.21   0.0272
PlaSh  -6.2080 [-16.1437;   3.7278] -1.22   0.2207
Plt   -21.3857 [-37.6485;  -5.1230] -2.58   0.0100
ReET  -20.3952 [-30.2320; -10.5584] -4.06 < 0.0001
rTMS  -13.0707 [-27.6199;   1.4784] -1.76   0.0783
tDCS  -10.5738 [-28.4976;   7.3500] -1.16   0.2476
WBV   -12.9142 [-25.5164;  -0.3120] -2.01   0.0446
WlNi         .                    .     .        .

Quantifying heterogeneity / inconsistency:
tau^2 = 154.5184; tau = 12.4305; I^2 = 92.7% [91.7%; 93.6%]

Tests of heterogeneity (within designs) and inconsistency (between designs):
                      Q d.f.  p-value
Total           1479.09  108 < 0.0001
Within designs   645.69   67 < 0.0001
Between designs  833.40   41 < 0.0001

Details of network meta-analysis methods:
- Frequentist graph-theoretical approach
- DerSimonian-Laird estimator for tau^2
- Calculation of I^2 based on Q

Code

# Network plot
netgraph(nma_sr,
    seq = "optimal",
    number.of.studies = TRUE,
    cex.points = n.trts,
    labels = paste0(trts, "\n(n=", round(n.trts), ")")
)

Network plot of treatments for Network (Q | Sh)

Code

# Detailed model summary
summary(nma_sr)

Original data (with adjusted standard errors for multi-arm studies):

                        treat1 treat2       TE    seTE seTE.adj narms multiarm
Albers2018                 Mnt   WlNi  -8.3900  5.6141  13.6395     2         
Alentorn-Geli2008          McT    WBV   6.4100  6.4532  16.7221     3        *
Alentorn-Geli2008          McT   WlNi  -9.4400  7.3279  17.6744     3        *
Alentorn-Geli2008          WBV   WlNi -15.8500  7.9606  18.5685     3        *
Alfano2001                 MfT  PlaSh  -4.6400  3.5345  12.9233     2         
Altan2004                 AqET    Bal  -1.8800  4.7088  13.2925     2         
Ardic2007                  Bal   WlNi  -8.6800  4.3832  13.1807     2         
Assis2006                 AeET   AqET   3.0000  5.0248  13.4077     2         
Assumpçao2018             FlET   ReET   9.1000  9.5318  19.9339     3        *
Assumpçao2018             FlET   WlNi -14.8000  8.1936  17.9047     3        *
Assumpçao2018             ReET   WlNi -23.9000  8.2018  17.9146     3        *
Astin2003                  CBT    McT   1.3000  4.1904  13.1178     2         
Baumueller2017             CBT   WlNi   2.3700  6.6855  14.1143     2         
Bongi2012                  CBT    McT  10.0900  6.8643  14.1999     2         
Bongi2010                  CBT   WlNi -13.9000  5.6413  13.6507     2         
Bourgault2015              McT   WlNi  -1.5700  4.9106  13.3654     2         
Boyer2014                PlaSh   rTMS  11.6000  4.3853  13.1814     2         
Calandre2009              AqET   FlET  -4.7200  4.2444  13.1352     2         
Carson2010                 McT   WlNi -13.2000  5.0135  13.4035     2         
Casanueva2014             DryN   WlNi  -9.7000  3.3866  12.8836     2         
Castro-Sanchez2019        DryN   MasT -23.8200  4.3700  13.1763     2         
Collado-Mateo2017         MiET  PlaSh  -6.9300  3.3233  12.8671     2         
daCosta2005               MiET   WlNi  -7.3000  4.2695  13.1433     2         
Dailey2019                Elec  PlaSh -56.0600 23.1621  26.2869     2         
deMedeiros2020            AqET    Plt   7.0000  5.0943  13.4339     2         
Ekici2017                 MasT    Plt   6.5600  3.3208  12.8665     2         
Espi-Lopes2016            MiET   WlNi   1.7600  7.0148  14.2733     2         
Evcik2002                  Bal   WlNi -33.8000 10.9424  16.5607     2         
Fernandes2016             AeET   AqET   3.4800  4.1605  13.1083     2         
Fitzgibbon2018           PlaSh   rTMS   3.0700  7.9323  14.7459     2         
Fonseca2019               AqET    CBT  13.6000  4.3355  13.1649     2         
Garcia2006                 CBT   WlNi -13.8800  9.9308  15.9103     2         
Garcia-Martinez2012       MiET   WlNi -18.2100  7.4089  14.4710     2         
Giannotti2014              McT   WlNi   4.5300  5.6870  13.6697     2         
Glasgow2017               ReET   WlNi -30.0000  7.0456  14.2884     2         
Gomez-Hernandez2019       AeET   MiET  10.6200  0.8775  12.4615     2         
Gowans2001                AqET   WlNi  -6.7300  5.8527  13.7395     2         
Hargrove2012             PlaSh   tDCS   9.9000  4.5112  13.2238     2         
Jones2002                  McT   ReET   5.5500  4.8917  13.3584     2         
Jones2012                  CBT    McT  13.4000  6.5801  14.0647     2         
Karatay2018                Acu  PlaSh -17.1100  4.8992  13.3612     2         
Kayo2012                  AeET   ReET  -9.7400  5.4131  16.6444     3        *
Kayo2012                  AeET   WlNi -16.1300  5.3516  16.5839     3        *
Kayo2012                  ReET   WlNi  -6.3900  5.2328  16.4708     3        *
King2002                   CBT    McT  11.6000  4.8603  13.3469     2         
Kurt2016                   Bal   MiET  -7.1000  2.5883  12.6972     2         
Lami2018                   CBT   WlNi   1.4400  3.3937  12.8855     2         
Lauche2016                MasT  PlaSh  -6.6000  2.8655  12.7565     2         
Lopes-Rodrigues2012       AqET   FlET -17.0700  4.6996  13.2893     2         
Lopes-Rodrigues2013       AqET   FlET -14.7900  4.2189  13.1270     2         
Luciano2014                CBT   WlNi -18.9800  1.5949  12.5324     2         
Lynch2012                  McT   WlNi -17.5200  3.2954  12.8599     2         
Mhalla2011               PlaSh   rTMS  10.7000  4.4529  13.2040     2         
Mist2018                   Acu    CBT -22.4000  5.0071  13.4011     2         
Olivares2011               WBV   WlNi  -3.7300  3.6133  12.9450     2         
Paolucci2016               MfT  PlaSh -22.3000  4.2735  13.1446     2         
Paolucci2015              MiET   WlNi  -9.7000  3.6907  12.9669     2         
Parra-Delgado2013          CBT   WlNi  -4.4300  5.5637  13.6188     2         
Pereira-Pernambuco2018     McT   WlNi -37.5900  3.2725  12.8541     2         
Perez-Aranda2019           CBT   WlNi  -6.8600  2.9360  12.7726     2         
Picard2013                 CBT   WlNi  -1.3500  4.6479  13.2711     2         
Redondo2004                CBT   MiET   4.3600  5.5606  13.6176     2         
Richards2002              AeET    McT   0.3000  2.6343  12.7066     2         
Rivera2018                 WBV   WlNi -22.0000  4.7523  13.3080     2         
Ruaro2014                  PbT  PlaSh -12.6000  3.8967  13.0270     2         
Salaffi2015                McT   WlNi  -8.2400  2.1713  12.6188     2         
Schachter2003             AeET   WlNi -10.1900  3.3527  12.8747     2         
Schmidt2011                CBT   WlNi  -3.0300  2.4137  12.6627     2         
Sevimli2015               AeET   AqET   1.7000  4.6428  16.2639     3        *
Sevimli2015               AeET   MiET -26.3000  4.4976  16.1420     3        *
Sevimli2015               AqET   MiET -28.0000  4.6973  16.3113     3        *
Silva2019                  CBT   ReET  25.7400  4.1233  13.0966     2         
Simister2018               CBT   WlNi -16.2300  3.2949  12.8598     2         
Soares2002                 CBT   WlNi  -1.8400  1.6938  12.5454     2         
Sutbeyaz2009               MfT  PlaSh -21.4000  3.5877  12.9379     2         
Tomas-Carus2007b&c        AqET   WlNi  -8.0000  6.1835  13.8836     2         
Ugurlu2017                 Acu  PlaSh -26.0300  3.5736  12.9340     2         
Valim2003                 AeET   FlET  -3.3100  4.8020  13.3258     2         
Vallejo2015                CBT   WlNi  -2.7600  4.8915  13.3583     2         
Vas2016                    Acu  PlaSh  -8.5000  2.5814  12.6958     2         
Verkaik2013                CBT   WlNi  -3.8000  3.3368  12.8706     2         
Wang2018                   McT   MiET  -6.8800  2.9934  12.7859     2         
Wicksell2013               CBT   WlNi  -4.8000  3.6440  12.9537     2         
Arakaki2021               FlET   ReET  15.7100  5.6273  13.6450     2         
Atan2020                  MiET   WlNi -31.0800  4.3638  13.1743     2         
Barranengoa-Cuadra2021     CBT   WlNi -24.1000  3.0388  12.7966     2         
Ceballos-Laita2020         McT   MiET  -0.3500  7.7076  14.6262     2         
Coste2021                  Mnt  PlaSh  -0.8000  4.0600  13.0768     2         
Izquierdo-Alventosa2020   MiET   WlNi  -5.5800  5.9339  13.7742     2         
Jamison2021               Elec  PlaSh  -7.4700  2.4770  12.6749     2         
Mingorance2021.2           WBV   WlNi  -8.2000  2.5870  12.6969     2         
Rodriguez-Mansilla2021     McT   MiET  -0.0800  4.2198  16.3621     3        *
Rodriguez-Mansilla2021    MiET   WlNi -11.6600  3.2126  15.6277     3        *
Rodriguez-Mansilla2021     McT   WlNi -11.7400  2.9326  15.4724     3        *
Sarmento2020               McT  PlaSh -18.0000  9.1302  15.4233     2         
Udina-Cortés2020          Elec  PlaSh  -9.3000  4.5740  13.2454     2         
Lacroix2022              PlaSh   rTMS   5.1500  3.5083  12.9161     2         
Paolucci2022               CBT   MiET  -8.5000  6.9387  14.2360     2         
Park2021                  FlET   ReET  11.3000  6.5679  14.0590     2         
Samartin-Veiga2022       PlaSh   tDCS   1.3400  5.3236  13.5226     2         
Alptug2023                 Mnt   WlNi -24.1000  5.9968  13.8015     2         
Audoux2023                MasT    Mnt -10.3000  7.1849  14.3576     2         
Baelz2022                  Acu  PlaSh  -2.6000  7.3763  14.4544     2         
Caumo2023                PlaSh   tDCS   1.8800  3.1792  12.8307     2         
Franco2023                AeET    Plt   4.5000  4.2791  13.1465     2         
Rodríguez-Mansilla2023    AeET    McT   0.0900  3.2027  15.8335     3        *
Rodríguez-Mansilla2023    AeET   WlNi -10.4800  2.6296  15.5088     3        *
Rodríguez-Mansilla2023     McT   WlNi -10.5700  2.6113  15.4998     3        *
Patru2021                  McT   MiET  11.8000  4.6757  16.3606     3        *
Patru2021                  McT   WlNi  -8.1000  4.5444  16.2456     3        *
Patru2021                 MiET   WlNi -19.9000  4.1244  15.9137     3        *
Lee2024                    CBT    McT   0.1000  2.6946  12.7193     2         
Schulze2023               FlET   MasT  17.4400  2.1681  15.4608     3        *
Schulze2023               MasT   WlNi -33.6200  2.0780  15.4237     3        *
Schulze2023               FlET   WlNi -16.1800  2.1603  15.4575     3        *
Agdere2025                AeET   WlNi -15.9000  3.9820  13.0528     2         
Anderson2025               CBT  PlaSh  -3.2000  7.5118  14.5240     2         
Badr2024                 PlaSh   rTMS   7.2000  3.8588  13.0157     2         
Carta2024                  CBT   WlNi  -6.1000  4.1260  13.0974     2         
Catella2024                CBT   WlNi  -6.9000  3.2961  12.8601     2         
Erol2025                   CBT   WlNi -21.0000  3.9813  13.0526     2         
Fernandes2024             FlET   ReET   8.7000  3.8347  13.0086     2         
Ferres2025                 McT   WlNi  -6.4000  3.7241  12.9764     2         
Gendreau2024               CBT  PlaSh  -8.1000  1.5739  12.5298     2         
Karakoyun2025              CBT   WlNi -46.1200  1.9235  12.5785     2         
Nhu2024                    CBT   WlNi  -6.6300  5.5983  13.6330     2         
PontesSilva2026           AeET   ReET   7.2000  5.3442  13.5307     2         
Ribeiro2023                PbT  PlaSh -12.8000  4.3288  13.1627     2         
RodriguezDominguez2026     McT   MiET  -1.4000  5.4653  13.5790     2         
Sari2025                   McT   WlNi   3.0000  7.0958  14.3133     2         
Silva2025                PlaSh   rTMS   2.9500  3.4045  12.8883     2         
Sevgin 2025               AeET   MiET -16.6000  0.7931  15.2054     3        *
Sevgin 2025               AeET    McT  10.5000  1.4577  15.3513     3        *
Sevgin 2025                McT   MiET -27.1000  1.4916  15.3612     3        *
Ughreja2024               MasT  PlaSh -11.8000  5.0295  16.4358     3        *
Ughreja2024               MasT   MiET  -7.1000  4.2639  15.8161     3        *
Ughreja2024               MiET  PlaSh  -4.7000  5.5425  16.9602     3        *
Vassao2024                 PbT   WlNi -19.8000  8.2322  14.9093     2         

Number of treatment arms per study (by decreasing number of arms):
                        narms multiarm
Alentorn-Geli2008           3        *
Assumpçao2018               3        *
Kayo2012                    3        *
Sevimli2015                 3        *
Rodriguez-Mansilla2021      3        *
Rodríguez-Mansilla2023      3        *
Patru2021                   3        *
Schulze2023                 3        *
Sevgin 2025                 3        *
Ughreja2024                 3        *
Albers2018                  2         
Alfano2001                  2         
Altan2004                   2         
Ardic2007                   2         
Assis2006                   2         
Astin2003                   2         
Baumueller2017              2         
Bongi2012                   2         
Bongi2010                   2         
Bourgault2015               2         
Boyer2014                   2         
Calandre2009                2         
Carson2010                  2         
Casanueva2014               2         
Castro-Sanchez2019          2         
Collado-Mateo2017           2         
daCosta2005                 2         
Dailey2019                  2         
deMedeiros2020              2         
Ekici2017                   2         
Espi-Lopes2016              2         
Evcik2002                   2         
Fernandes2016               2         
Fitzgibbon2018              2         
Fonseca2019                 2         
Garcia2006                  2         
Garcia-Martinez2012         2         
Giannotti2014               2         
Glasgow2017                 2         
Gomez-Hernandez2019         2         
Gowans2001                  2         
Hargrove2012                2         
Jones2002                   2         
Jones2012                   2         
Karatay2018                 2         
King2002                    2         
Kurt2016                    2         
Lami2018                    2         
Lauche2016                  2         
Lopes-Rodrigues2012         2         
Lopes-Rodrigues2013         2         
Luciano2014                 2         
Lynch2012                   2         
Mhalla2011                  2         
Mist2018                    2         
Olivares2011                2         
Paolucci2016                2         
Paolucci2015                2         
Parra-Delgado2013           2         
Pereira-Pernambuco2018      2         
Perez-Aranda2019            2         
Picard2013                  2         
Redondo2004                 2         
Richards2002                2         
Rivera2018                  2         
Ruaro2014                   2         
Salaffi2015                 2         
Schachter2003               2         
Schmidt2011                 2         
Silva2019                   2         
Simister2018                2         
Soares2002                  2         
Sutbeyaz2009                2         
Tomas-Carus2007b&c          2         
Ugurlu2017                  2         
Valim2003                   2         
Vallejo2015                 2         
Vas2016                     2         
Verkaik2013                 2         
Wang2018                    2         
Wicksell2013                2         
Arakaki2021                 2         
Atan2020                    2         
Barranengoa-Cuadra2021      2         
Ceballos-Laita2020          2         
Coste2021                   2         
Izquierdo-Alventosa2020     2         
Jamison2021                 2         
Mingorance2021.2            2         
Sarmento2020                2         
Udina-Cortés2020            2         
Lacroix2022                 2         
Paolucci2022                2         
Park2021                    2         
Samartin-Veiga2022          2         
Alptug2023                  2         
Audoux2023                  2         
Baelz2022                   2         
Caumo2023                   2         
Franco2023                  2         
Lee2024                     2         
Agdere2025                  2         
Anderson2025                2         
Badr2024                    2         
Carta2024                   2         
Catella2024                 2         
Erol2025                    2         
Fernandes2024               2         
Ferres2025                  2         
Gendreau2024                2         
Karakoyun2025               2         
Nhu2024                     2         
PontesSilva2026             2         
Ribeiro2023                 2         
RodriguezDominguez2026      2         
Sari2025                    2         
Silva2025                   2         
Vassao2024                  2         

Results (random effects model):

                        treat1 treat2       MD               95% CI
Albers2018                 Mnt   WlNi -11.4771 [-25.5926;   2.6384]
Alentorn-Geli2008          McT    WBV  -0.3658 [-13.8274;  13.0958]
Alentorn-Geli2008          McT   WlNi -13.2800 [-18.9144;  -7.6456]
Alentorn-Geli2008          WBV   WlNi -12.9142 [-25.5164;  -0.3120]
Alfano2001                 MfT  PlaSh -16.0396 [-30.7511;  -1.3281]
Altan2004                 AqET    Bal   1.2981 [-13.6900;  16.2861]
Ardic2007                  Bal   WlNi -16.5359 [-30.3793;  -2.6924]
Assis2006                 AeET   AqET   1.3593 [ -8.2762;  10.9947]
Assumpçao2018             FlET   ReET  13.0105 [  2.3708;  23.6501]
Assumpçao2018             FlET   WlNi  -7.3847 [-17.5890;   2.8195]
Assumpçao2018             ReET   WlNi -20.3952 [-30.2320; -10.5584]
Astin2003                  CBT    McT   3.7287 [ -2.7700;  10.2274]
Baumueller2017             CBT   WlNi  -9.5513 [-14.4435;  -4.6591]
Bongi2012                  CBT    McT   3.7287 [ -2.7700;  10.2274]
Bongi2010                  CBT   WlNi  -9.5513 [-14.4435;  -4.6591]
Bourgault2015              McT   WlNi -13.2800 [-18.9144;  -7.6456]
Boyer2014                PlaSh   rTMS   6.8628 [ -3.7655;  17.4910]
Calandre2009              AqET   FlET  -7.8530 [-18.4156;   2.7096]
Carson2010                 McT   WlNi -13.2800 [-18.9144;  -7.6456]
Casanueva2014             DryN   WlNi -24.8737 [-43.8033;  -5.9441]
Castro-Sanchez2019        DryN   MasT  -7.9490 [-26.9588;  11.0607]
Collado-Mateo2017         MiET  PlaSh  -3.2692 [-13.8020;   7.2637]
daCosta2005               MiET   WlNi  -9.4771 [-15.8232;  -3.1311]
Dailey2019                Elec  PlaSh -13.5088 [-30.4582;   3.4406]
deMedeiros2020            AqET    Plt   6.1479 [-10.1935;  22.4894]
Ekici2017                 MasT    Plt   4.4611 [-12.4971;  21.4193]
Espi-Lopes2016            MiET   WlNi  -9.4771 [-15.8232;  -3.1311]
Evcik2002                  Bal   WlNi -16.5359 [-30.3793;  -2.6924]
Fernandes2016             AeET   AqET   1.3593 [ -8.2762;  10.9947]
Fitzgibbon2018           PlaSh   rTMS   6.8628 [ -3.7655;  17.4910]
Fonseca2019               AqET    CBT  -5.6865 [-15.5004;   4.1274]
Garcia2006                 CBT   WlNi  -9.5513 [-14.4435;  -4.6591]
Garcia-Martinez2012       MiET   WlNi  -9.4771 [-15.8232;  -3.1311]
Giannotti2014              McT   WlNi -13.2800 [-18.9144;  -7.6456]
Glasgow2017               ReET   WlNi -20.3952 [-30.2320; -10.5584]
Gomez-Hernandez2019       AeET   MiET  -4.4014 [-13.0418;   4.2391]
Gowans2001                AqET   WlNi -15.2378 [-24.3866;  -6.0889]
Hargrove2012             PlaSh   tDCS   4.3658 [-10.5520;  19.2837]
Jones2002                  McT   ReET   7.1152 [ -3.4326;  17.6631]
Jones2012                  CBT    McT   3.7287 [ -2.7700;  10.2274]
Karatay2018                Acu  PlaSh -16.3468 [-28.1981;  -4.4956]
Kayo2012                  AeET   ReET   6.5167 [ -4.2179;  17.2514]
Kayo2012                  AeET   WlNi -13.8785 [-21.6321;  -6.1249]
Kayo2012                  ReET   WlNi -20.3952 [-30.2320; -10.5584]
King2002                   CBT    McT   3.7287 [ -2.7700;  10.2274]
Kurt2016                   Bal   MiET  -7.0587 [-21.2999;   7.1824]
Lami2018                   CBT   WlNi  -9.5513 [-14.4435;  -4.6591]
Lauche2016                MasT  PlaSh -10.7167 [-23.3392;   1.9059]
Lopes-Rodrigues2012       AqET   FlET  -7.8530 [-18.4156;   2.7096]
Lopes-Rodrigues2013       AqET   FlET  -7.8530 [-18.4156;   2.7096]
Luciano2014                CBT   WlNi  -9.5513 [-14.4435;  -4.6591]
Lynch2012                  McT   WlNi -13.2800 [-18.9144;  -7.6456]
Mhalla2011               PlaSh   rTMS   6.8628 [ -3.7655;  17.4910]
Mist2018                   Acu    CBT -13.0035 [-27.1843;   1.1773]
Olivares2011               WBV   WlNi -12.9142 [-25.5164;  -0.3120]
Paolucci2016               MfT  PlaSh -16.0396 [-30.7511;  -1.3281]
Paolucci2015              MiET   WlNi  -9.4771 [-15.8232;  -3.1311]
Parra-Delgado2013          CBT   WlNi  -9.5513 [-14.4435;  -4.6591]
Pereira-Pernambuco2018     McT   WlNi -13.2800 [-18.9144;  -7.6456]
Perez-Aranda2019           CBT   WlNi  -9.5513 [-14.4435;  -4.6591]
Picard2013                 CBT   WlNi  -9.5513 [-14.4435;  -4.6591]
Redondo2004                CBT   MiET  -0.0741 [ -7.3353;   7.1870]
Richards2002              AeET    McT  -0.5985 [ -9.0242;   7.8272]
Rivera2018                 WBV   WlNi -12.9142 [-25.5164;  -0.3120]
Ruaro2014                  PbT  PlaSh -12.9475 [-28.6101;   2.7150]
Salaffi2015                McT   WlNi -13.2800 [-18.9144;  -7.6456]
Schachter2003             AeET   WlNi -13.8785 [-21.6321;  -6.1249]
Schmidt2011                CBT   WlNi  -9.5513 [-14.4435;  -4.6591]
Sevimli2015               AeET   AqET   1.3593 [ -8.2762;  10.9947]
Sevimli2015               AeET   MiET  -4.4014 [-13.0418;   4.2391]
Sevimli2015               AqET   MiET  -5.7606 [-15.9045;   4.3832]
Silva2019                  CBT   ReET  10.8439 [  0.4266;  21.2613]
Simister2018               CBT   WlNi  -9.5513 [-14.4435;  -4.6591]
Soares2002                 CBT   WlNi  -9.5513 [-14.4435;  -4.6591]
Sutbeyaz2009               MfT  PlaSh -16.0396 [-30.7511;  -1.3281]
Tomas-Carus2007b&c        AqET   WlNi -15.2378 [-24.3866;  -6.0889]
Ugurlu2017                 Acu  PlaSh -16.3468 [-28.1981;  -4.4956]
Valim2003                 AeET   FlET  -6.4937 [-17.3702;   4.3827]
Vallejo2015                CBT   WlNi  -9.5513 [-14.4435;  -4.6591]
Vas2016                    Acu  PlaSh -16.3468 [-28.1981;  -4.4956]
Verkaik2013                CBT   WlNi  -9.5513 [-14.4435;  -4.6591]
Wang2018                   McT   MiET  -3.8029 [-10.8896;   3.2839]
Wicksell2013               CBT   WlNi  -9.5513 [-14.4435;  -4.6591]
Arakaki2021               FlET   ReET  13.0105 [  2.3708;  23.6501]
Atan2020                  MiET   WlNi  -9.4771 [-15.8232;  -3.1311]
Barranengoa-Cuadra2021     CBT   WlNi  -9.5513 [-14.4435;  -4.6591]
Ceballos-Laita2020         McT   MiET  -3.8029 [-10.8896;   3.2839]
Coste2021                  Mnt  PlaSh  -5.2691 [-20.3291;   9.7908]
Izquierdo-Alventosa2020   MiET   WlNi  -9.4771 [-15.8232;  -3.1311]
Jamison2021               Elec  PlaSh -13.5088 [-30.4582;   3.4406]
Mingorance2021.2           WBV   WlNi -12.9142 [-25.5164;  -0.3120]
Rodriguez-Mansilla2021     McT   MiET  -3.8029 [-10.8896;   3.2839]
Rodriguez-Mansilla2021    MiET   WlNi  -9.4771 [-15.8232;  -3.1311]
Rodriguez-Mansilla2021     McT   WlNi -13.2800 [-18.9144;  -7.6456]
Sarmento2020               McT  PlaSh  -7.0720 [-17.6935;   3.5495]
Udina-Cortés2020          Elec  PlaSh -13.5088 [-30.4582;   3.4406]
Lacroix2022              PlaSh   rTMS   6.8628 [ -3.7655;  17.4910]
Paolucci2022               CBT   MiET  -0.0741 [ -7.3353;   7.1870]
Park2021                  FlET   ReET  13.0105 [  2.3708;  23.6501]
Samartin-Veiga2022       PlaSh   tDCS   4.3658 [-10.5520;  19.2837]
Alptug2023                 Mnt   WlNi -11.4771 [-25.5926;   2.6384]
Audoux2023                MasT    Mnt  -5.4475 [-21.3390;  10.4439]
Baelz2022                  Acu  PlaSh -16.3468 [-28.1981;  -4.4956]
Caumo2023                PlaSh   tDCS   4.3658 [-10.5520;  19.2837]
Franco2023                AeET    Plt   7.5072 [ -8.6064;  23.6209]
Rodríguez-Mansilla2023    AeET    McT  -0.5985 [ -9.0242;   7.8272]
Rodríguez-Mansilla2023    AeET   WlNi -13.8785 [-21.6321;  -6.1249]
Rodríguez-Mansilla2023     McT   WlNi -13.2800 [-18.9144;  -7.6456]
Patru2021                  McT   MiET  -3.8029 [-10.8896;   3.2839]
Patru2021                  McT   WlNi -13.2800 [-18.9144;  -7.6456]
Patru2021                 MiET   WlNi  -9.4771 [-15.8232;  -3.1311]
Lee2024                    CBT    McT   3.7287 [ -2.7700;  10.2274]
Schulze2023               FlET   MasT   9.5399 [ -4.5184;  23.5982]
Schulze2023               MasT   WlNi -16.9246 [-28.5617;  -5.2875]
Schulze2023               FlET   WlNi  -7.3847 [-17.5890;   2.8195]
Agdere2025                AeET   WlNi -13.8785 [-21.6321;  -6.1249]
Anderson2025               CBT  PlaSh  -3.3433 [-13.3616;   6.6750]
Badr2024                 PlaSh   rTMS   6.8628 [ -3.7655;  17.4910]
Carta2024                  CBT   WlNi  -9.5513 [-14.4435;  -4.6591]
Catella2024                CBT   WlNi  -9.5513 [-14.4435;  -4.6591]
Erol2025                   CBT   WlNi  -9.5513 [-14.4435;  -4.6591]
Fernandes2024             FlET   ReET  13.0105 [  2.3708;  23.6501]
Ferres2025                 McT   WlNi -13.2800 [-18.9144;  -7.6456]
Gendreau2024               CBT  PlaSh  -3.3433 [-13.3616;   6.6750]
Karakoyun2025              CBT   WlNi  -9.5513 [-14.4435;  -4.6591]
Nhu2024                    CBT   WlNi  -9.5513 [-14.4435;  -4.6591]
PontesSilva2026           AeET   ReET   6.5167 [ -4.2179;  17.2514]
Ribeiro2023                PbT  PlaSh -12.9475 [-28.6101;   2.7150]
RodriguezDominguez2026     McT   MiET  -3.8029 [-10.8896;   3.2839]
Sari2025                   McT   WlNi -13.2800 [-18.9144;  -7.6456]
Silva2025                PlaSh   rTMS   6.8628 [ -3.7655;  17.4910]
Sevgin 2025               AeET   MiET  -4.4014 [-13.0418;   4.2391]
Sevgin 2025               AeET    McT  -0.5985 [ -9.0242;   7.8272]
Sevgin 2025                McT   MiET  -3.8029 [-10.8896;   3.2839]
Ughreja2024               MasT  PlaSh -10.7167 [-23.3392;   1.9059]
Ughreja2024               MasT   MiET  -7.4475 [-19.7535;   4.8584]
Ughreja2024               MiET  PlaSh  -3.2692 [-13.8020;   7.2637]
Vassao2024                 PbT   WlNi -19.1555 [-36.1579;  -2.1531]

Number of studies: k = 118
Number of pairwise comparisons: m = 138
Number of observations: o = 8059
Number of treatments: n = 21
Number of designs: d = 52

Random effects model

Treatment estimate (other treatments vs 'WlNi'):
            MD               95% CI     z  p-value
Acu   -22.5548 [-36.8555;  -8.2541] -3.09   0.0020
AeET  -13.8785 [-21.6321;  -6.1249] -3.51   0.0005
AqET  -15.2378 [-24.3866;  -6.0889] -3.26   0.0011
Bal   -16.5359 [-30.3793;  -2.6924] -2.34   0.0192
CBT    -9.5513 [-14.4435;  -4.6591] -3.83   0.0001
DryN  -24.8737 [-43.8033;  -5.9441] -2.58   0.0100
Elec  -19.7168 [-39.3637;  -0.0699] -1.97   0.0492
FlET   -7.3847 [-17.5890;   2.8195] -1.42   0.1561
MasT  -16.9246 [-28.5617;  -5.2875] -2.85   0.0044
McT   -13.2800 [-18.9144;  -7.6456] -4.62 < 0.0001
MfT   -22.2476 [-39.9999;  -4.4952] -2.46   0.0140
MiET   -9.4771 [-15.8232;  -3.1311] -2.93   0.0034
Mnt   -11.4771 [-25.5926;   2.6384] -1.59   0.1110
PbT   -19.1555 [-36.1579;  -2.1531] -2.21   0.0272
PlaSh  -6.2080 [-16.1437;   3.7278] -1.22   0.2207
Plt   -21.3857 [-37.6485;  -5.1230] -2.58   0.0100
ReET  -20.3952 [-30.2320; -10.5584] -4.06 < 0.0001
rTMS  -13.0707 [-27.6199;   1.4784] -1.76   0.0783
tDCS  -10.5738 [-28.4976;   7.3500] -1.16   0.2476
WBV   -12.9142 [-25.5164;  -0.3120] -2.01   0.0446
WlNi         .                    .     .        .

Quantifying heterogeneity / inconsistency:
tau^2 = 154.5184; tau = 12.4305; I^2 = 92.7% [91.7%; 93.6%]

Tests of heterogeneity (within designs) and inconsistency (between designs):
                      Q d.f.  p-value
Total           1479.09  108 < 0.0001
Within designs   645.69   67 < 0.0001
Between designs  833.40   41 < 0.0001

Details of network meta-analysis methods:
- Frequentist graph-theoretical approach
- DerSimonian-Laird estimator for tau^2
- Calculation of I^2 based on Q

Code

# Forest plot
forest(nma_sr)

Code

# Pairwise comparison table with confidence intervals
render_league_table(nma_sr)

Table 13: League table (random effects model) for Network

Comparison	Acu	AeET	AqET	Bal	CBT	DryN	Elec	FlET	MasT	McT	MfT	MiET	Mnt	PbT	PlaSh	Plt	ReET	rTMS	tDCS	WBV	WlNi
Acu	Acu	.	.	.	-22.4000 [-48.6657; 3.8657]	.	.	.	.	.	.	.	.	.	-14.0286 [-27.0748; -0.9824]	.	.	.	.	.	.
AeET	-8.6763 [-24.4858; 7.1332]	AeET	2.7310 [-12.2739; 17.7359]	.	.	.	.	-3.3100 [-29.4282; 22.8082]	.	3.7238 [-10.6295; 18.0771]	.	-10.1630 [-24.5312; 4.2052]	.	.	.	4.5000 [-21.2666; 30.2666]	-1.2529 [-20.0240; 17.5182]	.	.	.	-13.0657 [-25.8357; -0.2957]
AqET	-7.3170 [-23.8270; 9.1930]	1.3593 [ -8.2762; 10.9947]	AqET	-1.8800 [-27.9329; 24.1729]	13.6000 [-12.2027; 39.4027]	.	.	-12.1567 [-27.0746; 2.7612]	.	.	.	-28.0000 [-54.0449; -1.9551]	.	.	.	7.0000 [-19.3300; 33.3300]	.	.	.	.	-7.3584 [-26.4990; 11.7823]
Bal	-6.0189 [-25.6344; 13.5965]	2.6574 [-12.5310; 17.8457]	1.2981 [-13.6900; 16.2861]	Bal	.	.	.	.	.	.	.	-7.1000 [-31.9860; 17.7860]	.	.	.	.	.	.	.	.	-18.4216 [-38.6347; 1.7914]
CBT	-13.0035 [-27.1843; 1.1773]	-4.3272 [-12.9897; 4.3353]	-5.6865 [-15.5004; 4.1274]	-6.9846 [-21.4488; 7.4796]	CBT	.	.	.	.	6.9176 [ -4.8755; 18.7108]	.	-1.7846 [-21.0715; 17.5023]	.	.	-6.0092 [-24.6039; 12.5854]	.	25.7400 [ 0.0712; 51.4088]	.	.	.	-10.1449 [-15.9066; -4.3832]
DryN	2.3189 [-20.7550; 25.3928]	10.9952 [ -9.1623; 31.1527]	9.6359 [-11.0265; 30.2983]	8.3378 [-14.9537; 31.6294]	15.3224 [ -4.0856; 34.7304]	DryN	.	.	-23.8200 [-49.6451; 2.0051]	.	.	.	.	.	.	.	.	.	.	.	-9.7000 [-34.9514; 15.5514]
Elec	-2.8380 [-23.5198; 17.8438]	5.8383 [-14.8989; 26.5755]	4.4790 [-16.8050; 25.7630]	3.1809 [-20.5892; 26.9510]	10.1655 [ -9.5233; 29.8543]	-5.1569 [-31.7608; 21.4470]	Elec	.	.	.	.	.	.	.	-13.5088 [-30.4582; 3.4406]	.	.	.	.	.	.
FlET	-15.1700 [-32.2668; 1.9268]	-6.4937 [-17.3702; 4.3827]	-7.8530 [-18.4156; 2.7096]	-9.1511 [-25.6023; 7.3001]	-2.1665 [-13.0396; 8.7066]	-17.4889 [-38.5120; 3.5342]	-12.3320 [-34.0619; 9.3979]	FlET	17.4400 [ -7.2912; 42.1712]	.	.	.	.	.	.	.	11.2684 [ -2.4490; 24.9859]	.	.	.	-15.6032 [-34.4686; 3.2622]
MasT	-5.6301 [-22.2933; 11.0330]	3.0462 [-10.0205; 16.1128]	1.6869 [-12.0330; 15.4067]	0.3888 [-17.2681; 18.0457]	7.3734 [ -4.7885; 19.5352]	-7.9490 [-26.9588; 11.0607]	-2.7921 [-23.9253; 18.3411]	9.5399 [ -4.5184; 23.5982]	MasT	.	.	-7.1000 [-32.8569; 18.6569]	-10.3000 [-38.4404; 17.8404]	.	-9.0703 [-27.1852; 9.0445]	6.5600 [-18.6578; 31.7778]	.	.	.	.	-33.6200 [-58.3215; -8.9185]
McT	-9.2748 [-24.0883; 5.5387]	-0.5985 [ -9.0242; 7.8272]	-1.9578 [-12.0586; 8.1431]	-3.2559 [-17.8661; 11.3544]	3.7287 [ -2.7700; 10.2274]	-11.5937 [-31.1893; 8.0019]	-6.4368 [-26.4393; 13.5657]	5.8952 [ -5.1603; 16.9508]	-3.6446 [-16.0830; 8.7937]	McT	.	-4.6312 [-15.2501; 5.9877]	.	.	-18.0000 [-48.2291; 12.2291]	.	5.5500 [-20.6320; 31.7320]	.	.	6.4100 [-21.0408; 33.8608]	-10.1472 [-17.6328; -2.6616]
MfT	-0.3072 [-19.1985; 18.5841]	8.3691 [-10.5829; 27.3211]	7.0098 [-12.5389; 26.5585]	5.7117 [-16.5181; 27.9415]	12.6963 [ -5.1024; 30.4950]	-2.6261 [-27.8632; 22.6110]	2.5308 [-19.9127; 24.9743]	14.8628 [ -5.1705; 34.8961]	5.3229 [-14.0615; 24.7074]	8.9676 [ -9.1775; 27.1127]	MfT	.	.	.	-16.0396 [-30.7511; -1.3281]	.	.	.	.	.	.
MiET	-13.0777 [-27.9104; 1.7551]	-4.4014 [-13.0418; 4.2391]	-5.7606 [-15.9045; 4.3832]	-7.0587 [-21.2999; 7.1824]	-0.0741 [ -7.3353; 7.1870]	-15.3966 [-35.0624; 4.2693]	-10.2396 [-30.1952; 9.7159]	2.0924 [ -9.2143; 13.3990]	-7.4475 [-19.7535; 4.8584]	-3.8029 [-10.8896; 3.2839]	-12.7704 [-30.8638; 5.3229]	MiET	.	.	-5.8775 [-24.2034; 12.4483]	.	.	.	.	.	-12.9316 [-22.2385; -3.6246]
Mnt	-11.0777 [-29.6505; 7.4952]	-2.4014 [-18.1909; 13.3882]	-3.7607 [-20.2331; 12.7118]	-5.0587 [-24.6533; 14.5358]	1.9258 [-12.7188; 16.5704]	-13.3966 [-36.1437; 9.3506]	-8.2397 [-30.9131; 14.4337]	4.0924 [-12.9052; 21.0900]	-5.4475 [-21.3390; 10.4439]	-1.8029 [-16.7468; 13.1410]	-10.7705 [-31.8235; 10.2825]	2.0000 [-13.0435; 17.0435]	Mnt	.	-0.8000 [-26.4300; 24.8300]	.	.	.	.	.	-16.1523 [-35.1666; 2.8620]
PbT	-3.3993 [-22.7931; 15.9945]	5.2770 [-13.0966; 23.6506]	3.9177 [-15.0753; 22.9107]	2.6196 [-19.0970; 24.3363]	9.6042 [ -7.6275; 26.8359]	-5.7182 [-30.6385; 19.2021]	-0.5613 [-23.6394; 22.5168]	11.7707 [ -7.7295; 31.2710]	2.2309 [-17.0179; 21.4796]	5.8755 [-11.6770; 23.4280]	-3.0921 [-24.5803; 18.3962]	9.6784 [ -7.9032; 27.2599]	7.6784 [-13.2255; 28.5822]	PbT	-12.6990 [-30.8464; 5.4485]	.	.	.	.	.	-19.8000 [-49.0217; 9.4217]
PlaSh	-16.3468 [-28.1981; -4.4956]	-7.6705 [-19.6183; 4.2772]	-9.0298 [-21.9032; 3.8436]	-10.3279 [-26.9933; 6.3375]	-3.3433 [-13.3616; 6.6750]	-18.6657 [-39.1714; 1.8400]	-13.5088 [-30.4582; 3.4406]	-1.1768 [-14.7748; 12.4212]	-10.7167 [-23.3392; 1.9059]	-7.0720 [-17.6935; 3.5495]	-16.0396 [-30.7511; -1.3281]	-3.2692 [-13.8020; 7.2637]	-5.2691 [-20.3291; 9.7908]	-12.9475 [-28.6101; 2.7150]	PlaSh	.	.	6.8628 [ -3.7655; 17.4910]	4.3658 [-10.5520; 19.2837]	.	.
Plt	-1.1691 [-22.0844; 19.7462]	7.5072 [ -8.6064; 23.6209]	6.1479 [-10.1935; 22.4894]	4.8499 [-15.8287; 25.5284]	11.8344 [ -4.8334; 28.5023]	-3.4880 [-27.3188; 20.3429]	1.6689 [-23.1055; 26.4434]	14.0010 [ -3.7301; 31.7320]	4.4611 [-12.4971; 21.4193]	8.1057 [ -8.6471; 24.8585]	-0.8618 [-24.1625; 22.4388]	11.9086 [ -4.8549; 28.6721]	9.9086 [-10.7821; 30.5993]	2.2302 [-20.7278; 25.1882]	15.1778 [ -2.8913; 33.2468]	Plt	.	.	.	.	.
ReET	-2.1596 [-19.1202; 14.8011]	6.5167 [ -4.2179; 17.2514]	5.1574 [ -6.6950; 17.0099]	3.8594 [-12.6804; 20.3992]	10.8439 [ 0.4266; 21.2613]	-4.4785 [-25.5514; 16.5944]	0.6784 [-20.9733; 22.3302]	13.0105 [ 2.3708; 23.6501]	3.4706 [-11.0079; 17.9491]	7.1152 [ -3.4326; 17.6631]	-1.8523 [-21.8009; 18.0962]	10.9181 [ -0.1564; 21.9926]	8.9181 [ -8.0244; 25.8606]	1.2397 [-18.1450; 20.6244]	14.1873 [ 0.7144; 27.6601]	-0.9905 [-19.0671; 17.0861]	ReET	.	.	.	-19.4461 [-35.5004; -3.3918]
rTMS	-9.4841 [-25.4030; 6.4349]	-0.8078 [-16.7986; 15.1831]	-2.1671 [-18.8609; 14.5268]	-3.4651 [-23.2311; 16.3009]	3.5194 [-11.0862; 18.1251]	-11.8030 [-34.8993; 11.2934]	-6.6461 [-26.6521; 13.3600]	5.6860 [-11.5728; 22.9447]	-3.8539 [-20.3551; 12.6472]	-0.2093 [-15.2351; 14.8166]	-9.1768 [-27.3259; 8.9722]	3.5936 [-11.3697; 18.5569]	1.5936 [-16.8390; 20.0262]	-6.0848 [-25.0129; 12.8434]	6.8628 [ -3.7655; 17.4910]	-8.3150 [-29.2780; 12.6481]	-7.3245 [-24.4848; 9.8358]	rTMS	.	.	.
tDCS	-11.9810 [-31.0334; 7.0715]	-3.3047 [-22.4173; 15.8079]	-4.6640 [-24.3685; 15.0405]	-5.9620 [-28.3289; 16.4049]	1.0225 [-16.9471; 18.9922]	-14.2999 [-39.6578; 11.0581]	-9.1430 [-31.7223; 13.4364]	3.1891 [-16.9963; 23.3744]	-6.3508 [-25.8924; 13.1907]	-2.7062 [-21.0190; 15.6066]	-11.6738 [-32.6254; 9.2779]	1.0967 [-17.1649; 19.3582]	-0.9033 [-22.1010; 20.2944]	-8.5817 [-30.2117; 13.0484]	4.3658 [-10.5520; 19.2837]	-10.8119 [-34.2434; 12.6195]	-9.8214 [-29.9227; 10.2798]	-2.4969 [-20.8136; 15.8198]	tDCS	.	.
WBV	-9.6406 [-28.6367; 9.3554]	-0.9643 [-15.6565; 13.7279]	-2.3236 [-17.8330; 13.1858]	-3.6217 [-22.3031; 15.0597]	3.3629 [-10.0822; 16.8079]	-11.9595 [-34.6806; 10.7616]	-6.8026 [-30.0882; 16.4830]	5.5294 [-10.6240; 21.6828]	-4.0105 [-21.1103; 13.0893]	-0.3658 [-13.8274; 13.0958]	-9.3334 [-31.0443; 12.3775]	3.4370 [-10.5586; 17.4327]	1.4371 [-17.4556; 20.3297]	-6.2413 [-27.3606; 14.8779]	6.7062 [ -9.2605; 22.6729]	-8.4716 [-28.9899; 12.0468]	-7.4811 [-23.3882; 8.4261]	-0.1566 [-19.3371; 19.0240]	2.3404 [-19.5109; 24.1916]	WBV	-12.0540 [-25.1477; 1.0396]
WlNi	-22.5548 [-36.8555; -8.2541]	-13.8785 [-21.6321; -6.1249]	-15.2378 [-24.3866; -6.0889]	-16.5359 [-30.3793; -2.6924]	-9.5513 [-14.4435; -4.6591]	-24.8737 [-43.8033; -5.9441]	-19.7168 [-39.3637; -0.0699]	-7.3847 [-17.5890; 2.8195]	-16.9246 [-28.5617; -5.2875]	-13.2800 [-18.9144; -7.6456]	-22.2476 [-39.9999; -4.4952]	-9.4771 [-15.8232; -3.1311]	-11.4771 [-25.5926; 2.6384]	-19.1555 [-36.1579; -2.1531]	-6.2080 [-16.1437; 3.7278]	-21.3857 [-37.6485; -5.1230]	-20.3952 [-30.2320; -10.5584]	-13.0707 [-27.6199; 1.4784]	-10.5738 [-28.4976; 7.3500]	-12.9142 [-25.5164; -0.3120]	WlNi

Code

# Treatment ranking
ranking_sr <- netrank(nma_sr, small.values = "good")
print(ranking_sr)

      P-score
DryN   0.8038
Acu    0.7855
MfT    0.7543
ReET   0.7437
Plt    0.7356
Elec   0.6694
PbT    0.6636
MasT   0.6077
Bal    0.5852
AqET   0.5484
AeET   0.4890
McT    0.4657
rTMS   0.4531
WBV    0.4490
Mnt    0.3934
tDCS   0.3693
CBT    0.2836
MiET   0.2830
FlET   0.2230
PlaSh  0.1685
WlNi   0.0250

Code

# Rankogram
ran_sr <- rankogram(nma_sr)
plot(ran_sr)

Code

# Design decomposition
decomp_sr <- decomp.design(nma_sr)
print(decomp_sr)

Q statistics to assess homogeneity / consistency

                      Q  df  p-value
Total           1479.09 108 < 0.0001
Within designs   645.69  67 < 0.0001
Between designs  833.40  41 < 0.0001

Design-specific decomposition of within-designs Q statistic

        Design      Q df  p-value
      WlNi:CBT 449.22 19 < 0.0001
      WlNi:McT  86.28  7 < 0.0001
     WlNi:MiET  26.23  5 < 0.0001
     Acu:PlaSh  18.66  3   0.0003
     MfT:PlaSh  14.67  2   0.0007
      WlNi:WBV   9.70  2   0.0078
      WlNi:Bal   4.54  1   0.0331
      WlNi:Mnt   3.66  1   0.0558
       CBT:McT   7.78  4   0.0999
     AqET:FlET   4.54  2   0.1033
    Elec:PlaSh   4.41  2   0.1101
 WlNi:McT:MiET   3.92  2   0.1407
      CBT:MiET   2.09  1   0.1481
     WlNi:AeET   1.20  1   0.2727
    PlaSh:tDCS   2.40  2   0.3006
     CBT:PlaSh   0.41  1   0.5232
      McT:MiET   1.19  2   0.5511
     FlET:ReET   1.06  2   0.5879
    PlaSh:rTMS   3.69  5   0.5950
     WlNi:AqET   0.02  1   0.8814
     AeET:AqET   0.01  1   0.9413
     PbT:PlaSh   0.00  1   0.9726

Between-designs Q statistic after detaching of single designs
(influential designs have p-value markedly different from < 0.0001)

 Detached design      Q df  p-value
   AeET:McT:MiET 277.66 39 < 0.0001
       AeET:MiET 369.14 40 < 0.0001
  WlNi:FlET:MasT 765.55 39 < 0.0001
       DryN:MasT 789.91 40 < 0.0001
       WlNi:DryN 789.91 40 < 0.0001
  AeET:AqET:MiET 803.20 39 < 0.0001
        CBT:ReET 810.97 40 < 0.0001
   WlNi:McT:MiET 811.31 39 < 0.0001
        AqET:CBT 818.14 40 < 0.0001
  WlNi:AeET:ReET 815.83 39 < 0.0001
        WlNi:McT 820.78 40 < 0.0001
      MasT:PlaSh 824.83 40 < 0.0001
       WlNi:MiET 825.70 40 < 0.0001
         Acu:CBT 825.93 40 < 0.0001
       Acu:PlaSh 825.93 40 < 0.0001
       AqET:FlET 826.44 40 < 0.0001
      MiET:PlaSh 828.55 40 < 0.0001
       WlNi:AqET 828.98 40 < 0.0001
       CBT:PlaSh 829.34 40 < 0.0001
        MasT:Plt 829.54 40 < 0.0001
        AeET:Plt 829.55 40 < 0.0001
        WlNi:WBV 830.06 40 < 0.0001
        AeET:McT 830.55 40 < 0.0001
   WlNi:AeET:McT 827.78 39 < 0.0001
        McT:MiET 831.07 40 < 0.0001
        WlNi:Mnt 831.77 40 < 0.0001
       WlNi:ReET 831.96 40 < 0.0001
        CBT:MiET 832.00 40 < 0.0001
       Mnt:PlaSh 832.03 40 < 0.0001
       FlET:ReET 832.22 40 < 0.0001
    WlNi:McT:WBV 829.09 39 < 0.0001
       McT:PlaSh 832.38 40 < 0.0001
        WlNi:CBT 832.97 40 < 0.0001
         CBT:McT 833.05 40 < 0.0001
        WlNi:Bal 833.09 40 < 0.0001
        Bal:MiET 833.10 40 < 0.0001
       AeET:ReET 833.11 40 < 0.0001
       WlNi:AeET 833.34 40 < 0.0001
        MasT:Mnt 833.37 40 < 0.0001
        AqET:Plt 833.37 40 < 0.0001
       AeET:AqET 833.38 40 < 0.0001
        AqET:Bal 833.39 40 < 0.0001
       PbT:PlaSh 833.39 40 < 0.0001
        WlNi:PbT 833.39 40 < 0.0001
        McT:ReET 833.39 40 < 0.0001
       AeET:FlET 833.40 40 < 0.0001
 MasT:MiET:PlaSh 831.00 39 < 0.0001
  WlNi:FlET:ReET 832.80 39 < 0.0001

Q statistic to assess consistency under the assumption of
a full design-by-treatment interaction random effects model

                    Q df p-value tau.within tau2.within
Between designs 28.94 41  0.9214    11.3504    128.8326

Code

# Node-splitting analysis to detect inconsistency
split_sr <- netsplit(nma_sr,) #show = "all")
render_netsplit_table(split_sr)

Table 14: Node-splitting analysis for Network

comparison	TE	seTE	lower	upper	statistic	p
Acu:AeET	-8.6763	8.0662	-24.4858	7.1332	-1.0756	0.2821
Acu:AqET	-7.3170	8.4236	-23.8270	9.1930	-0.8686	0.3850
Acu:Bal	-6.0189	10.0081	-25.6344	13.5965	-0.6014	0.5476
Acu:CBT	-13.0035	7.2352	-27.1843	1.1773	-1.7972	0.0723
Acu:DryN	2.3189	11.7726	-20.7550	25.3928	0.1970	0.8438
Acu:Elec	-2.8380	10.5521	-23.5198	17.8438	-0.2690	0.7880
Acu:FlET	-15.1700	8.7230	-32.2668	1.9268	-1.7391	0.0820
Acu:MasT	-5.6301	8.5018	-22.2933	11.0330	-0.6622	0.5078
Acu:McT	-9.2748	7.5580	-24.0883	5.5387	-1.2271	0.2198
Acu:MfT	-0.3072	9.6386	-19.1985	18.5841	-0.0319	0.9746
Acu:MiET	-13.0777	7.5679	-27.9104	1.7551	-1.7280	0.0840
Acu:Mnt	-11.0777	9.4761	-29.6505	7.4952	-1.1690	0.2424
Acu:PbT	-3.3993	9.8950	-22.7931	15.9945	-0.3435	0.7312
Acu:PlaSh	-16.3468	6.0467	-28.1981	-4.4956	-2.7034	0.0069
Acu:Plt	-1.1691	10.6713	-22.0844	19.7462	-0.1096	0.9128
Acu:ReET	-2.1596	8.6535	-19.1202	14.8011	-0.2496	0.8029
Acu:rTMS	-9.4841	8.1220	-25.4030	6.4349	-1.1677	0.2429
Acu:tDCS	-11.9810	9.7208	-31.0334	7.0715	-1.2325	0.2178
Acu:WBV	-9.6406	9.6920	-28.6367	9.3554	-0.9947	0.3199
Acu:WlNi	-22.5548	7.2964	-36.8555	-8.2541	-3.0912	0.0020
AeET:AqET	1.3593	4.9161	-8.2762	10.9947	0.2765	0.7822
AeET:Bal	2.6574	7.7493	-12.5310	17.8457	0.3429	0.7317
AeET:CBT	-4.3272	4.4197	-12.9897	4.3353	-0.9791	0.3275
AeET:DryN	10.9952	10.2846	-9.1623	31.1527	1.0691	0.2850
AeET:Elec	5.8383	10.5804	-14.8989	26.5755	0.5518	0.5811
AeET:FlET	-6.4937	5.5493	-17.3702	4.3827	-1.1702	0.2419
AeET:MasT	3.0462	6.6668	-10.0205	16.1128	0.4569	0.6477
AeET:McT	-0.5985	4.2989	-9.0242	7.8272	-0.1392	0.8893
AeET:MfT	8.3691	9.6695	-10.5829	27.3211	0.8655	0.3868
AeET:MiET	-4.4014	4.4085	-13.0418	4.2391	-0.9984	0.3181
AeET:Mnt	-2.4014	8.0560	-18.1909	13.3882	-0.2981	0.7656
AeET:PbT	5.2770	9.3745	-13.0966	23.6506	0.5629	0.5735
AeET:PlaSh	-7.6705	6.0959	-19.6183	4.2772	-1.2583	0.2083
AeET:Plt	7.5072	8.2214	-8.6064	23.6209	0.9131	0.3612
AeET:ReET	6.5167	5.4770	-4.2179	17.2514	1.1898	0.2341
AeET:rTMS	-0.8078	8.1588	-16.7986	15.1831	-0.0990	0.9211
AeET:tDCS	-3.3047	9.7515	-22.4173	15.8079	-0.3389	0.7347
AeET:WBV	-0.9643	7.4962	-15.6565	13.7279	-0.1286	0.8976
AeET:WlNi	-13.8785	3.9560	-21.6321	-6.1249	-3.5082	0.0005
AqET:Bal	1.2981	7.6471	-13.6900	16.2861	0.1697	0.8652
AqET:CBT	-5.6865	5.0072	-15.5004	4.1274	-1.1357	0.2561
AqET:DryN	9.6359	10.5422	-11.0265	30.2983	0.9140	0.3607
AqET:Elec	4.4790	10.8594	-16.8050	25.7630	0.4125	0.6800
AqET:FlET	-7.8530	5.3892	-18.4156	2.7096	-1.4572	0.1451
AqET:MasT	1.6869	7.0001	-12.0330	15.4067	0.2410	0.8096
AqET:McT	-1.9578	5.1536	-12.0586	8.1431	-0.3799	0.7040
AqET:MfT	7.0098	9.9740	-12.5389	26.5585	0.7028	0.4822
AqET:MiET	-5.7606	5.1755	-15.9045	4.3832	-1.1131	0.2657
AqET:Mnt	-3.7607	8.4045	-20.2331	12.7118	-0.4475	0.6545
AqET:PbT	3.9177	9.6905	-15.0753	22.9107	0.4043	0.6860
AqET:PlaSh	-9.0298	6.5682	-21.9032	3.8436	-1.3748	0.1692
AqET:Plt	6.1479	8.3376	-10.1935	22.4894	0.7374	0.4609
AqET:ReET	5.1574	6.0473	-6.6950	17.0099	0.8529	0.3937
AqET:rTMS	-2.1671	8.5174	-18.8609	14.5268	-0.2544	0.7992
AqET:tDCS	-4.6640	10.0535	-24.3685	15.0405	-0.4639	0.6427
AqET:WBV	-2.3236	7.9131	-17.8330	13.1858	-0.2936	0.7690
AqET:WlNi	-15.2378	4.6679	-24.3866	-6.0889	-3.2644	0.0011
Bal:CBT	-6.9846	7.3798	-21.4488	7.4796	-0.9464	0.3439
Bal:DryN	8.3378	11.8836	-14.9537	31.6294	0.7016	0.4829
Bal:Elec	3.1809	12.1278	-20.5892	26.9510	0.2623	0.7931
Bal:FlET	-9.1511	8.3936	-25.6023	7.3001	-1.0902	0.2756
Bal:MasT	0.3888	9.0088	-17.2681	18.0457	0.0432	0.9656
Bal:McT	-3.2559	7.4543	-17.8661	11.3544	-0.4368	0.6623
Bal:MfT	5.7117	11.3419	-16.5181	27.9415	0.5036	0.6145
Bal:MiET	-7.0587	7.2660	-21.2999	7.1824	-0.9715	0.3313
Bal:Mnt	-5.0587	9.9974	-24.6533	14.5358	-0.5060	0.6129
Bal:PbT	2.6196	11.0801	-19.0970	24.3363	0.2364	0.8131
Bal:PlaSh	-10.3279	8.5029	-26.9933	6.3375	-1.2146	0.2245
Bal:Plt	4.8499	10.5505	-15.8287	25.5284	0.4597	0.6457
Bal:ReET	3.8594	8.4388	-12.6804	20.3992	0.4573	0.6474
Bal:rTMS	-3.4651	10.0849	-23.2311	16.3009	-0.3436	0.7311
Bal:tDCS	-5.9620	11.4119	-28.3289	16.4049	-0.5224	0.6014
Bal:WBV	-3.6217	9.5315	-22.3031	15.0597	-0.3800	0.7040
Bal:WlNi	-16.5359	7.0631	-30.3793	-2.6924	-2.3412	0.0192
CBT:DryN	15.3224	9.9022	-4.0856	34.7304	1.5474	0.1218
CBT:Elec	10.1655	10.0455	-9.5233	29.8543	1.0119	0.3116
CBT:FlET	-2.1665	5.5476	-13.0396	8.7066	-0.3905	0.6961
CBT:MasT	7.3734	6.2052	-4.7885	19.5352	1.1883	0.2347
CBT:McT	3.7287	3.3157	-2.7700	10.2274	1.1246	0.2608
CBT:MfT	12.6963	9.0811	-5.1024	30.4950	1.3981	0.1621
CBT:MiET	-0.0741	3.7048	-7.3353	7.1870	-0.0200	0.9840
CBT:Mnt	1.9258	7.4719	-12.7188	16.5704	0.2577	0.7966
CBT:PbT	9.6042	8.7918	-7.6275	26.8359	1.0924	0.2747
CBT:PlaSh	-3.3433	5.1115	-13.3616	6.6750	-0.6541	0.5131
CBT:Plt	11.8344	8.5041	-4.8334	28.5023	1.3916	0.1640
CBT:ReET	10.8439	5.3151	0.4266	21.2613	2.0402	0.0413
CBT:rTMS	3.5194	7.4520	-11.0862	18.1251	0.4723	0.6367
CBT:tDCS	1.0225	9.1684	-16.9471	18.9922	0.1115	0.9112
CBT:WBV	3.3629	6.8598	-10.0822	16.8079	0.4902	0.6240
CBT:WlNi	-9.5513	2.4961	-14.4435	-4.6591	-3.8265	0.0001
DryN:Elec	-5.1569	13.5737	-31.7608	21.4470	-0.3799	0.7040
DryN:FlET	-17.4889	10.7263	-38.5120	3.5342	-1.6305	0.1030
DryN:MasT	-7.9490	9.6990	-26.9588	11.0607	-0.8196	0.4125
DryN:McT	-11.5937	9.9979	-31.1893	8.0019	-1.1596	0.2462
DryN:MfT	-2.6261	12.8763	-27.8632	22.6110	-0.2039	0.8384
DryN:MiET	-15.3966	10.0338	-35.0624	4.2693	-1.5345	0.1249
DryN:Mnt	-13.3966	11.6059	-36.1437	9.3506	-1.1543	0.2484
DryN:PbT	-5.7182	12.7147	-30.6385	19.2021	-0.4497	0.6529
DryN:PlaSh	-18.6657	10.4623	-39.1714	1.8400	-1.7841	0.0744
DryN:Plt	-3.4880	12.1588	-27.3188	20.3429	-0.2869	0.7742
DryN:ReET	-4.4785	10.7517	-25.5514	16.5944	-0.4165	0.6770
DryN:rTMS	-11.8030	11.7841	-34.8993	11.2934	-1.0016	0.3165
DryN:tDCS	-14.2999	12.9380	-39.6578	11.0581	-1.1053	0.2690
DryN:WBV	-11.9595	11.5926	-34.6806	10.7616	-1.0316	0.3022
DryN:WlNi	-24.8737	9.6581	-43.8033	-5.9441	-2.5754	0.0100
Elec:FlET	-12.3320	11.0869	-34.0619	9.3979	-1.1123	0.2660
Elec:MasT	-2.7921	10.7824	-23.9253	18.3411	-0.2590	0.7957
Elec:McT	-6.4368	10.2055	-26.4393	13.5657	-0.6307	0.5282
Elec:MfT	2.5308	11.4510	-19.9127	24.9743	0.2210	0.8251
Elec:MiET	-10.2396	10.1816	-30.1952	9.7159	-1.0057	0.3146
Elec:Mnt	-8.2397	11.5683	-30.9131	14.4337	-0.7123	0.4763
Elec:PbT	-0.5613	11.7748	-23.6394	22.5168	-0.0477	0.9620
Elec:PlaSh	-13.5088	8.6478	-30.4582	3.4406	-1.5621	0.1183
Elec:Plt	1.6689	12.6403	-23.1055	26.4434	0.1320	0.8950
Elec:ReET	0.6784	11.0470	-20.9733	22.3302	0.0614	0.9510
Elec:rTMS	-6.6461	10.2074	-26.6521	13.3600	-0.6511	0.5150
Elec:tDCS	-9.1430	11.5203	-31.7223	13.4364	-0.7936	0.4274
Elec:WBV	-6.8026	11.8806	-30.0882	16.4830	-0.5726	0.5669
Elec:WlNi	-19.7168	10.0241	-39.3637	-0.0699	-1.9669	0.0492
FlET:MasT	9.5399	7.1728	-4.5184	23.5982	1.3300	0.1835
FlET:McT	5.8952	5.6407	-5.1603	16.9508	1.0451	0.2960
FlET:MfT	14.8628	10.2213	-5.1705	34.8961	1.4541	0.1459
FlET:MiET	2.0924	5.7688	-9.2143	13.3990	0.3627	0.7168
FlET:Mnt	4.0924	8.6724	-12.9052	21.0900	0.4719	0.6370
FlET:PbT	11.7707	9.9493	-7.7295	31.2710	1.1831	0.2368
FlET:PlaSh	-1.1768	6.9379	-14.7748	12.4212	-0.1696	0.8653
FlET:Plt	14.0010	9.0466	-3.7301	31.7320	1.5476	0.1217
FlET:ReET	13.0105	5.4285	2.3708	23.6501	2.3967	0.0165
FlET:rTMS	5.6860	8.8057	-11.5728	22.9447	0.6457	0.5185
FlET:tDCS	3.1891	10.2988	-16.9963	23.3744	0.3097	0.7568
FlET:WBV	5.5294	8.2417	-10.6240	21.6828	0.6709	0.5023
FlET:WlNi	-7.3847	5.2063	-17.5890	2.8195	-1.4184	0.1561
MasT:McT	-3.6446	6.3462	-16.0830	8.7937	-0.5743	0.5658
MasT:MfT	5.3229	9.8902	-14.0615	24.7074	0.5382	0.5904
MasT:MiET	-7.4475	6.2787	-19.7535	4.8584	-1.1862	0.2356
MasT:Mnt	-5.4475	8.1080	-21.3390	10.4439	-0.6719	0.5017
MasT:PbT	2.2309	9.8210	-17.0179	21.4796	0.2272	0.8203
MasT:PlaSh	-10.7167	6.4402	-23.3392	1.9059	-1.6640	0.0961
MasT:Plt	4.4611	8.6523	-12.4971	21.4193	0.5156	0.6061
MasT:ReET	3.4706	7.3871	-11.0079	17.9491	0.4698	0.6385
MasT:rTMS	-3.8539	8.4191	-20.3551	12.6472	-0.4578	0.6471
MasT:tDCS	-6.3508	9.9704	-25.8924	13.1907	-0.6370	0.5241
MasT:WBV	-4.0105	8.7246	-21.1103	13.0893	-0.4597	0.6457
MasT:WlNi	-16.9246	5.9374	-28.5617	-5.2875	-2.8505	0.0044
McT:MfT	8.9676	9.2579	-9.1775	27.1127	0.9686	0.3327
McT:MiET	-3.8029	3.6157	-10.8896	3.2839	-1.0518	0.2929
McT:Mnt	-1.8029	7.6246	-16.7468	13.1410	-0.2365	0.8131
McT:PbT	5.8755	8.9555	-11.6770	23.4280	0.6561	0.5118
McT:PlaSh	-7.0720	5.4192	-17.6935	3.5495	-1.3050	0.1919
McT:Plt	8.1057	8.5475	-8.6471	24.8585	0.9483	0.3430
McT:ReET	7.1152	5.3817	-3.4326	17.6631	1.3221	0.1861
McT:rTMS	-0.2093	7.6664	-15.2351	14.8166	-0.0273	0.9782
McT:tDCS	-2.7062	9.3435	-21.0190	15.6066	-0.2896	0.7721
McT:WBV	-0.3658	6.8683	-13.8274	13.0958	-0.0533	0.9575
McT:WlNi	-13.2800	2.8748	-18.9144	-7.6456	-4.6195	0.0000
MfT:MiET	-12.7704	9.2315	-30.8638	5.3229	-1.3834	0.1666
MfT:Mnt	-10.7705	10.7415	-31.8235	10.2825	-1.0027	0.3160
MfT:PbT	-3.0921	10.9636	-24.5803	18.3962	-0.2820	0.7779
MfT:PlaSh	-16.0396	7.5060	-30.7511	-1.3281	-2.1369	0.0326
MfT:Plt	-0.8618	11.8883	-24.1625	22.4388	-0.0725	0.9422
MfT:ReET	-1.8523	10.1780	-21.8009	18.0962	-0.1820	0.8556
MfT:rTMS	-9.1768	9.2599	-27.3259	8.9722	-0.9910	0.3217
MfT:tDCS	-11.6738	10.6898	-32.6254	9.2779	-1.0920	0.2748
MfT:WBV	-9.3334	11.0772	-31.0443	12.3775	-0.8426	0.3995
MfT:WlNi	-22.2476	9.0575	-39.9999	-4.4952	-2.4563	0.0140
MiET:Mnt	2.0000	7.6754	-13.0435	17.0435	0.2606	0.7944
MiET:PbT	9.6784	8.9703	-7.9032	27.2599	1.0789	0.2806
MiET:PlaSh	-3.2692	5.3740	-13.8020	7.2637	-0.6083	0.5430
MiET:Plt	11.9086	8.5530	-4.8549	28.6721	1.3923	0.1638
MiET:ReET	10.9181	5.6504	-0.1564	21.9926	1.9323	0.0533
MiET:rTMS	3.5936	7.6345	-11.3697	18.5569	0.4707	0.6379
MiET:tDCS	1.0967	9.3173	-17.1649	19.3582	0.1177	0.9063
MiET:WBV	3.4370	7.1408	-10.5586	17.4327	0.4813	0.6303
MiET:WlNi	-9.4771	3.2378	-15.8232	-3.1311	-2.9270	0.0034
Mnt:PbT	7.6784	10.6654	-13.2255	28.5822	0.7199	0.4716
Mnt:PlaSh	-5.2691	7.6838	-20.3291	9.7908	-0.6857	0.4929
Mnt:Plt	9.9086	10.5567	-10.7821	30.5993	0.9386	0.3479
Mnt:ReET	8.9181	8.6443	-8.0244	25.8606	1.0317	0.3022
Mnt:rTMS	1.5936	9.4046	-16.8390	20.0262	0.1695	0.8654
Mnt:tDCS	-0.9033	10.8154	-22.1010	20.2944	-0.0835	0.9334
Mnt:WBV	1.4371	9.6393	-17.4556	20.3297	0.1491	0.8815
Mnt:WlNi	-11.4771	7.2019	-25.5926	2.6384	-1.5936	0.1110
PbT:PlaSh	-12.9475	7.9912	-28.6101	2.7150	-1.6202	0.1052
PbT:Plt	2.2302	11.7135	-20.7278	25.1882	0.1904	0.8490
PbT:ReET	1.2397	9.8903	-18.1450	20.6244	0.1253	0.9002
PbT:rTMS	-6.0848	9.6574	-25.0129	12.8434	-0.6301	0.5287
PbT:tDCS	-8.5817	11.0359	-30.2117	13.0484	-0.7776	0.4368
PbT:WBV	-6.2413	10.7753	-27.3606	14.8779	-0.5792	0.5624
PbT:WlNi	-19.1555	8.6748	-36.1579	-2.1531	-2.2082	0.0272
PlaSh:Plt	15.1778	9.2191	-2.8913	33.2468	1.6463	0.0997
PlaSh:ReET	14.1873	6.8740	0.7144	27.6601	2.0639	0.0390
PlaSh:rTMS	6.8628	5.4227	-3.7655	17.4910	1.2656	0.2057
PlaSh:tDCS	4.3658	7.6113	-10.5520	19.2837	0.5736	0.5662
PlaSh:WBV	6.7062	8.1464	-9.2605	22.6729	0.8232	0.4104
PlaSh:WlNi	-6.2080	5.0693	-16.1437	3.7278	-1.2246	0.2207
Plt:ReET	-0.9905	9.2229	-19.0671	17.0861	-0.1074	0.9145
Plt:rTMS	-8.3150	10.6956	-29.2780	12.6481	-0.7774	0.4369
Plt:tDCS	-10.8119	11.9550	-34.2434	12.6195	-0.9044	0.3658
Plt:WBV	-8.4716	10.4687	-28.9899	12.0468	-0.8092	0.4184
Plt:WlNi	-21.3857	8.2975	-37.6485	-5.1230	-2.5774	0.0100
ReET:rTMS	-7.3245	8.7554	-24.4848	9.8358	-0.8366	0.4028
ReET:tDCS	-9.8214	10.2559	-29.9227	10.2798	-0.9576	0.3382
ReET:WBV	-7.4811	8.1161	-23.3882	8.4261	-0.9218	0.3567
ReET:WlNi	-20.3952	5.0189	-30.2320	-10.5584	-4.0637	0.0000
rTMS:tDCS	-2.4969	9.3454	-20.8136	15.8198	-0.2672	0.7893
rTMS:WBV	-0.1566	9.7862	-19.3371	19.0240	-0.0160	0.9872
rTMS:WlNi	-13.0707	7.4232	-27.6199	1.4784	-1.7608	0.0783
tDCS:WBV	2.3404	11.1488	-19.5109	24.1916	0.2099	0.8337
tDCS:WlNi	-10.5738	9.1450	-28.4976	7.3500	-1.1562	0.2476
WBV:WlNi	-12.9142	6.4298	-25.5164	-0.3120	-2.0085	0.0446

Code

# Forest plot of node-splitting
plot_netsplit_forest(split_sr)

Figure 7: Forest plot of node-splitting for Network

Code

# Heatmap
netheat(nma_sr)

Code

# Funnel plot
funnel(nma_sr,
    order = "WlNi",
)

Code

# Read data
data_qlg <- read_excel("data/Banco de Dados_Rstudio (1).xlsx", sheet = "Q | Lg") |>
    mutate(
        Mean1 = as.numeric(Mean1),
        Mean2 = as.numeric(Mean2),
        Mean3 = as.numeric(Mean3),
        SD1 = as.numeric(SD1),
        SD2 = as.numeric(SD2),
        SD3 = as.numeric(SD3)
    )


# Transform to contrast-based
pw <- pairwise(
    treat = list(Treat1, Treat2, Treat3),
    n = list(N1, N2, N3),
    mean = list(Mean1, Mean2, Mean3),
    sd = list(SD1, SD2, SD3),
    studlab = StudyID,
    data = data_qlg,
    sm      = "MD"
)

# Check network connections
net_con <- netconnection(pw)
net_con

Number of studies: k = 28
Number of pairwise comparisons: m = 32
Number of treatments: n = 11
Number of designs: d = 16
Number of networks: 2

Details on subnetworks: 
 subnetwork  k  m n
          1  3  3 3
          2 25 29 8

There are two sub-networks:

Subnet 1:
- 3 studies
- 3 comparisons
- 3 treatments

Subnet 2:
- 25 studies
- 29 comparisons
- 8 treatments

Tip

There are two treatment sub-networks that do not connect.

Note

Please: Select the treatment sub-networks before proceeding.

Select the procedures performed

Important

The first subnet contains 3 studies, 3 comparisons and 3 treatments.

Code

# Filter data for subnet 1
sr1 <- tibble(
    treat1 = net_con$treat1,
    treat2 = net_con$treat2,
    subnet = net_con$subnet
) |>
    filter(subnet == 1)

# Display comparison table
sr1

Code

# Unique treatments in subnet 1
unique_treat1 <- sr1$treat1 |> unique()
unique_treat2 <- sr1$treat2 |> unique()

# Combination of all unique treatments
trat_sr1 <- unique(c(unique_treat1, unique_treat2))
print(trat_sr1)

[1] "MfT"   "rTMS"  "PlaSh"

Code

# Total number of treatments
length(trat_sr1)

[1] 3

Code

# Filter pairwise object only for comparisons within subnet 1
pw_qlg_sr1 <- pw %>%
    filter(treat1 %in% trat_sr1 & treat2 %in% trat_sr1)

# Number of comparisons in subnet 1
nrow(pw_qlg_sr1)

[1] 3

Code

# Fit NMA model for subnet 1
nma_sr1 <- netmeta(
    TE,
    seTE,
    treat1,
    treat2,
    studlab,
    data = pw_qlg_sr1,
    n1 = n1,
    n2 = n2,
    random = TRUE,
    common = FALSE,
    sm = "MD", # can be "MD" or "SMD"
    ref = "PlaSh" # Reference treatment (change if you want another control)
)

# Basic model summary
print(nma_sr1)

Number of studies: k = 3
Number of pairwise comparisons: m = 3
Number of observations: o = 235
Number of treatments: n = 3
Number of designs: d = 2

Random effects model

Treatment estimate (other treatments vs 'PlaSh'):
           MD             95% CI     z p-value
MfT   -5.5600 [-13.2259; 2.1059] -1.42  0.1552
PlaSh       .                  .     .       .
rTMS  -3.7055 [ -8.8789; 1.4679] -1.40  0.1604

Quantifying heterogeneity / inconsistency:
tau^2 = 0; tau = 0; I^2 = 0%

Tests of heterogeneity (within designs) and inconsistency (between designs):
                   Q d.f. p-value
Total           0.65    1  0.4209
Within designs  0.65    1  0.4209
Between designs 0.00    0      --

Details of network meta-analysis methods:
- Frequentist graph-theoretical approach
- DerSimonian-Laird estimator for tau^2
- Calculation of I^2 based on Q

Code

# Network plot
netgraph(
    nma_sr1,
    seq = "optimal",
    number.of.studies = TRUE,
    cex.points = n.trts,
    labels = paste0(trts, "\n(n=", round(n.trts), ")")
)

Network plot of treatments for Subnet 1 (Q | Lg)

Code

# Detailed model summary
summary(nma_sr1)

Original data:

           treat1 treat2      TE   seTE
Alfano2001    MfT  PlaSh -5.5600 3.9112
Mhalla2011  PlaSh   rTMS  7.3000 5.1879
Silva2025   PlaSh   rTMS  2.4500 3.0661

Number of treatment arms per study:
           narms
Alfano2001     2
Mhalla2011     2
Silva2025      2

Results (random effects model):

           treat1 treat2      MD             95% CI
Alfano2001    MfT  PlaSh -5.5600 [-13.2259; 2.1059]
Mhalla2011  PlaSh   rTMS  3.7055 [ -1.4679; 8.8789]
Silva2025   PlaSh   rTMS  3.7055 [ -1.4679; 8.8789]

Number of studies: k = 3
Number of pairwise comparisons: m = 3
Number of observations: o = 235
Number of treatments: n = 3
Number of designs: d = 2

Random effects model

Treatment estimate (other treatments vs 'PlaSh'):
           MD             95% CI     z p-value
MfT   -5.5600 [-13.2259; 2.1059] -1.42  0.1552
PlaSh       .                  .     .       .
rTMS  -3.7055 [ -8.8789; 1.4679] -1.40  0.1604

Quantifying heterogeneity / inconsistency:
tau^2 = 0; tau = 0; I^2 = 0%

Tests of heterogeneity (within designs) and inconsistency (between designs):
                   Q d.f. p-value
Total           0.65    1  0.4209
Within designs  0.65    1  0.4209
Between designs 0.00    0      --

Details of network meta-analysis methods:
- Frequentist graph-theoretical approach
- DerSimonian-Laird estimator for tau^2
- Calculation of I^2 based on Q

Code

# Forest plot
forest(nma_sr1)

Code

# Pairwise comparison table with confidence intervals
render_league_table(nma_sr1)

Table 15: League table (random effects model) for Subnet 1

Comparison	MfT	PlaSh	rTMS
MfT	MfT	-5.5600 [-13.2259; 2.1059]	.
PlaSh	-5.5600 [-13.2259; 2.1059]	PlaSh	3.7055 [ -1.4679; 8.8789]
rTMS	-1.8545 [-11.1027; 7.3937]	3.7055 [ -1.4679; 8.8789]	rTMS

Code

# Treatment ranking
ranking_sr1 <- netrank(nma_sr1, small.values = "good")
print(ranking_sr1)

      P-score
MfT    0.7876
rTMS   0.6335
PlaSh  0.0789

Code

# Rankogram
ran_sr1 <- rankogram(nma_sr1)
plot(ran_sr1)

Code

# Design decomposition
decomp_sr1 <- decomp.design(nma_sr1)
print(decomp_sr1)

Q statistics to assess homogeneity / consistency

                   Q df p-value
Total           0.65  1  0.4209
Within designs  0.65  1  0.4209
Between designs 0.00  0      --

Design-specific decomposition of within-designs Q statistic

     Design    Q df p-value
 PlaSh:rTMS 0.65  1  0.4209

Q statistic to assess consistency under the assumption of
a full design-by-treatment interaction random effects model

                   Q df p-value tau.within tau2.within
Between designs 0.00  0      --          0           0

Code

# Node-splitting analysis to detect inconsistency
split_sr1 <- netsplit(nma_sr1, show = "all")
render_netsplit_table(split_sr1)

Table 16: Node-splitting analysis for Subnet 1

comparison	TE	seTE	lower	upper	statistic	p
MfT:PlaSh	-5.5600	3.9112	-13.2259	2.1059	-1.4215	0.1552
MfT:rTMS	-1.8545	4.7186	-11.1027	7.3937	-0.3930	0.6943
rTMS:PlaSh	-3.7055	2.6396	-8.8789	1.4679	-1.4038	0.1604

Code

# Forest plot of node-splitting
plot_netsplit_forest(split_sr1)

Figure 8: Forest plot of node-splitting for Subnet 1

Code

# Heatmap
netheat(nma_sr1)

Code

# Funnel plot
funnel(nma_sr1, order = "PlaSh", )

Select the procedures performed

Important

The second subnet is more robust, containing 28 studies, 29 comparisons and 8 treatments. This is the main analysis network.

Code

# Filter data for subnet 2
sr2 <- tibble(
    treat1 = net_con$treat1,
    treat2 = net_con$treat2,
    subnet = net_con$subnet
) |>
    filter(subnet == 2)

# Display first rows of comparison table
sr2 |> head()

Code

# Unique treatments in subnet 2
unique_treat1_sr2 <- sr2$treat1 |> unique()
print(unique_treat1_sr2)

[1] "AeET" "AqET" "MiET" "ReET" "McT"  "CBT"

Code

unique_treat2_sr2 <- sr2$treat2 |> unique()
print(unique_treat2_sr2)

[1] "AqET" "WlNi" "ReET" "MiET" "McT"  "FlET" "AeET"

Code

# Combination of all unique treatments
trat_sr2 <- unique(c(unique_treat1_sr2, unique_treat2_sr2))
print(trat_sr2)

[1] "AeET" "AqET" "MiET" "ReET" "McT"  "CBT"  "WlNi" "FlET"

Code

# Total number of treatments
length(trat_sr2)

[1] 8

Code

# Filter pairwise object only for comparisons within subnet 2
pw_qlg_sr2 <- pw %>%
    filter(treat1 %in% trat_sr2 & treat2 %in% trat_sr2)

# Number of unique studies
length(unique(pw_qlg_sr2$studlab))

[1] 25

Code

# Fit NMA model for subnet 2
nma_sr2 <- netmeta(
    TE,
    seTE,
    treat1,
    treat2,
    studlab,
    data = pw_qlg_sr2,
    n1 = N1,
    n2 = N2,
    random = TRUE,
    common = FALSE,
    sm = "MD",
    ref = "WlNi" # Reference treatment (change if you want another control)
)

# Basic model summary
print(nma_sr2)

Number of studies: k = 25
Number of pairwise comparisons: m = 29
Number of observations: o = 1598
Number of treatments: n = 8
Number of designs: d = 14

Random effects model

Treatment estimate (other treatments vs 'WlNi'):
           MD               95% CI     z  p-value
AeET -11.8420 [-20.2902;  -3.3937] -2.75   0.0060
AqET -13.9340 [-23.5710;  -4.2969] -2.83   0.0046
CBT  -13.9298 [-27.5247;  -0.3349] -2.01   0.0446
FlET  -3.2089 [-18.5220;  12.1042] -0.41   0.6813
McT  -19.8856 [-30.9618;  -8.8093] -3.52   0.0004
MiET -22.9301 [-33.1470; -12.7131] -4.40 < 0.0001
ReET -12.6444 [-27.1692;   1.8804] -1.71   0.0880
WlNi        .                    .     .        .

Quantifying heterogeneity / inconsistency:
tau^2 = 110.5191; tau = 10.5128; I^2 = 86.4% [80.6%; 90.5%]

Tests of heterogeneity (within designs) and inconsistency (between designs):
                     Q d.f.  p-value
Total           147.45   20 < 0.0001
Within designs   60.27   11 < 0.0001
Between designs  87.18    9 < 0.0001

Details of network meta-analysis methods:
- Frequentist graph-theoretical approach
- DerSimonian-Laird estimator for tau^2
- Calculation of I^2 based on Q

Code

# Network plot
netgraph(
    nma_sr2,
    seq = "optimal",
    number.of.studies = TRUE,
    cex.points = n.trts,
    labels = paste0(trts, "\n(n=", round(n.trts), ")")
)

Network plot of treatments for Subnet 2 (Q | Lg)

Code

# Detailed model summary
summary(nma_sr2)

Original data (with adjusted standard errors for multi-arm studies):

                       treat1 treat2       TE   seTE seTE.adj narms multiarm
Acosta-Gallego2018       AeET   AqET   8.1000 4.1615  11.3065     2         
Andrade2019              AqET   WlNi -14.6000 3.3374  11.0298     2         
Assis2006                AeET   AqET   4.4600 5.1075  11.6878     2         
Baptista2012             AeET   WlNi -17.6300 3.3515  11.0341     2         
Etnier2009               MiET   WlNi -25.1800 9.0950  13.9010     2         
Kayo2012                 AeET   ReET  -5.6500 6.0952  15.3700     3        *
Kayo2012                 AeET   WlNi -18.9200 4.8426  13.9002     3        *
Kayo2012                 ReET   WlNi -13.2700 5.1235  14.1662     3        *
Larsson2015               McT   MiET   4.9000 3.3376  11.0299     2         
Letieri2013              AqET   WlNi -24.2400 7.3707  12.8392     2         
Mannerkorpi2000           McT   WlNi -11.9400 4.6207  11.4835     2         
Mannerkorpi2004           McT   WlNi   1.8300 5.4792  11.8550     2         
Munguia-Izquierdo 2007   AqET   WlNi   0.4000 3.4557  11.0662     2         
Rooks2007                 CBT    McT   8.3000 3.6542  13.7803     3        *
Rooks2007                 CBT   MiET   4.7500 3.3682  13.5479     3        *
Rooks2007                 McT   MiET  -3.5500 2.7539  13.1427     3        *
Sanudo2010b              AeET   MiET   0.0000 3.7033  11.1460     2         
Sanudo2011               MiET   WlNi -64.8400 5.5331  11.8800     2         
Sanudo2010c              AeET   MiET  11.9900 5.8375  12.0248     2         
Schachter2003            AeET   WlNi  -2.3000 2.8448  10.8909     2         
Tomas-Carus2008          AqET   WlNi -11.9400 4.4726  11.4247     2         
Valim2003                AeET   FlET  -9.0900 4.1151  11.2895     2         
Wang2018                  McT   MiET -11.6700 3.5984  11.1116     2         
Hernando-Garijo2021      AeET   WlNi  -2.9000 6.1852  12.1974     2         
Saranya2022               CBT   FlET  -6.8100 1.6075  10.6350     2         
Erol2025                  CBT   WlNi -16.9000 3.8020  11.1792     2         
Fernandes2024            FlET   ReET  13.4000 4.1710  11.3100     2         
RodriguezDominguez2026    McT   MiET  -5.5000 5.5800  11.9019     2         
PontesSilva2026          AeET   ReET  -0.1000 4.7175  11.5228     2         

Number of treatment arms per study (by decreasing number of arms):
                       narms multiarm
Kayo2012                   3        *
Rooks2007                  3        *
Acosta-Gallego2018         2         
Andrade2019                2         
Assis2006                  2         
Baptista2012               2         
Etnier2009                 2         
Larsson2015                2         
Letieri2013                2         
Mannerkorpi2000            2         
Mannerkorpi2004            2         
Munguia-Izquierdo 2007     2         
Sanudo2010b                2         
Sanudo2011                 2         
Sanudo2010c                2         
Schachter2003              2         
Tomas-Carus2008            2         
Valim2003                  2         
Wang2018                   2         
Hernando-Garijo2021        2         
Saranya2022                2         
Erol2025                   2         
Fernandes2024              2         
RodriguezDominguez2026     2         
PontesSilva2026            2         

Results (random effects model):

                       treat1 treat2       MD               95% CI
Acosta-Gallego2018       AeET   AqET   2.0920 [ -8.7007;  12.8847]
Andrade2019              AqET   WlNi -13.9340 [-23.5710;  -4.2969]
Assis2006                AeET   AqET   2.0920 [ -8.7007;  12.8847]
Baptista2012             AeET   WlNi -11.8420 [-20.2902;  -3.3937]
Etnier2009               MiET   WlNi -22.9301 [-33.1470; -12.7131]
Kayo2012                 AeET   ReET   0.8024 [-12.7533;  14.3582]
Kayo2012                 AeET   WlNi -11.8420 [-20.2902;  -3.3937]
Kayo2012                 ReET   WlNi -12.6444 [-27.1692;   1.8804]
Larsson2015               McT   MiET   3.0445 [ -6.6365;  12.7255]
Letieri2013              AqET   WlNi -13.9340 [-23.5710;  -4.2969]
Mannerkorpi2000           McT   WlNi -19.8856 [-30.9618;  -8.8093]
Mannerkorpi2004           McT   WlNi -19.8856 [-30.9618;  -8.8093]
Munguia-Izquierdo 2007   AqET   WlNi -13.9340 [-23.5710;  -4.2969]
Rooks2007                 CBT    McT   5.9558 [ -8.7336;  20.6451]
Rooks2007                 CBT   MiET   9.0003 [ -5.1924;  23.1930]
Rooks2007                 McT   MiET   3.0445 [ -6.6365;  12.7255]
Sanudo2010b              AeET   MiET  11.0881 [  0.2126;  21.9636]
Sanudo2011               MiET   WlNi -22.9301 [-33.1470; -12.7131]
Sanudo2010c              AeET   MiET  11.0881 [  0.2126;  21.9636]
Schachter2003            AeET   WlNi -11.8420 [-20.2902;  -3.3937]
Tomas-Carus2008          AqET   WlNi -13.9340 [-23.5710;  -4.2969]
Valim2003                AeET   FlET  -8.6331 [-23.1554;   5.8893]
Wang2018                  McT   MiET   3.0445 [ -6.6365;  12.7255]
Hernando-Garijo2021      AeET   WlNi -11.8420 [-20.2902;  -3.3937]
Saranya2022               CBT   FlET -10.7209 [-26.3698;   4.9280]
Erol2025                  CBT   WlNi -13.9298 [-27.5247;  -0.3349]
Fernandes2024            FlET   ReET   9.4355 [ -6.3990;  25.2700]
RodriguezDominguez2026    McT   MiET   3.0445 [ -6.6365;  12.7255]
PontesSilva2026          AeET   ReET   0.8024 [-12.7533;  14.3582]

Number of studies: k = 25
Number of pairwise comparisons: m = 29
Number of observations: o = 1598
Number of treatments: n = 8
Number of designs: d = 14

Random effects model

Treatment estimate (other treatments vs 'WlNi'):
           MD               95% CI     z  p-value
AeET -11.8420 [-20.2902;  -3.3937] -2.75   0.0060
AqET -13.9340 [-23.5710;  -4.2969] -2.83   0.0046
CBT  -13.9298 [-27.5247;  -0.3349] -2.01   0.0446
FlET  -3.2089 [-18.5220;  12.1042] -0.41   0.6813
McT  -19.8856 [-30.9618;  -8.8093] -3.52   0.0004
MiET -22.9301 [-33.1470; -12.7131] -4.40 < 0.0001
ReET -12.6444 [-27.1692;   1.8804] -1.71   0.0880
WlNi        .                    .     .        .

Quantifying heterogeneity / inconsistency:
tau^2 = 110.5191; tau = 10.5128; I^2 = 86.4% [80.6%; 90.5%]

Tests of heterogeneity (within designs) and inconsistency (between designs):
                     Q d.f.  p-value
Total           147.45   20 < 0.0001
Within designs   60.27   11 < 0.0001
Between designs  87.18    9 < 0.0001

Details of network meta-analysis methods:
- Frequentist graph-theoretical approach
- DerSimonian-Laird estimator for tau^2
- Calculation of I^2 based on Q

Code

# Forest plot
forest(nma_sr2)

Code

# Pairwise comparison table with confidence intervals
render_league_table(nma_sr2)

Table 17: League table (random effects model) for Subnet 2

Comparison	AeET	AqET	CBT	FlET	McT	MiET	ReET	WlNi
AeET	AeET	6.3403 [ -9.5870; 22.2677]	.	-9.0900 [-31.2170; 13.0370]	.	5.5409 [-10.4807; 21.5625]	-2.7276 [-19.1157; 13.6605]	-10.5388 [-21.7012; 0.6237]
AqET	2.0920 [ -8.7007; 12.8847]	AqET	.	.	.	.	.	-11.7976 [-23.0922; -0.5031]
CBT	2.0878 [-12.2937; 16.4693]	-0.0042 [-16.1651; 16.1568]	CBT	-6.8100 [-27.6542; 14.0342]	8.3000 [-13.5140; 30.1140]	4.7500 [-16.8865; 26.3865]	.	-16.9000 [-38.8108; 5.0108]
FlET	-8.6331 [-23.1554; 5.8893]	-10.7251 [-27.9178; 6.4676]	-10.7209 [-26.3698; 4.9280]	FlET	.	.	13.4000 [ -8.7672; 35.5672]	.
McT	8.0436 [ -4.3918; 20.4790]	5.9516 [ -8.2740; 20.1772]	5.9558 [ -8.7336; 20.6451]	16.6767 [ -0.6320; 33.9853]	McT	-3.8664 [-14.8494; 7.1167]	.	-5.2742 [-21.4404; 10.8921]
MiET	11.0881 [ 0.2126; 21.9636]	8.9961 [ -4.3465; 22.3387]	9.0003 [ -5.1924; 23.1930]	19.7212 [ 3.1872; 36.2551]	3.0445 [ -6.6365; 12.7255]	MiET	.	-48.1000 [-65.8009; -30.3991]
ReET	0.8024 [-12.7533; 14.3582]	-1.2896 [-17.7473; 15.1681]	-1.2854 [-19.0798; 16.5090]	9.4355 [ -6.3990; 25.2700]	-7.2412 [-24.4589; 9.9766]	-10.2857 [-26.6252; 6.0538]	ReET	-13.2700 [-36.1915; 9.6515]
WlNi	-11.8420 [-20.2902; -3.3937]	-13.9340 [-23.5710; -4.2969]	-13.9298 [-27.5247; -0.3349]	-3.2089 [-18.5220; 12.1042]	-19.8856 [-30.9618; -8.8093]	-22.9301 [-33.1470; -12.7131]	-12.6444 [-27.1692; 1.8804]	WlNi

Code

# Treatment ranking
ranking_sr2 <- netrank(nma_sr2, small.values = "good")
print(ranking_sr2)

     P-score
MiET  0.9128
McT   0.7875
AqET  0.5565
CBT   0.5538
ReET  0.5110
AeET  0.4563
FlET  0.1632
WlNi  0.0589

Code

# Rankogram
ran_sr2 <- rankogram(nma_sr2)
plot(ran_sr2)

Code

# Design decomposition
decomp_sr2 <- decomp.design(nma_sr2)
print(decomp_sr2)

Q statistics to assess homogeneity / consistency

                     Q df  p-value
Total           147.45 20 < 0.0001
Within designs   60.27 11 < 0.0001
Between designs  87.18  9 < 0.0001

Design-specific decomposition of within-designs Q statistic

    Design     Q df p-value
 WlNi:MiET 13.88  1  0.0002
 WlNi:AeET 12.95  2  0.0015
 WlNi:AqET 14.84  3  0.0020
  McT:MiET 11.60  2  0.0030
  WlNi:McT  3.69  1  0.0547
 AeET:MiET  3.01  1  0.0828
 AeET:AqET  0.31  1  0.5806

Between-designs Q statistic after detaching of single designs
(influential designs have p-value markedly different from < 0.0001)

 Detached design     Q df  p-value
       WlNi:MiET 17.99  8   0.0213
        WlNi:McT 65.44  8 < 0.0001
       AeET:AqET 82.29  8 < 0.0001
       WlNi:AqET 82.29  8 < 0.0001
       AeET:MiET 83.00  8 < 0.0001
        McT:MiET 83.31  8 < 0.0001
       WlNi:AeET 83.66  8 < 0.0001
        CBT:FlET 83.77  8 < 0.0001
        WlNi:CBT 84.75  8 < 0.0001
       FlET:ReET 85.19  8 < 0.0001
  WlNi:AeET:ReET 83.68  7 < 0.0001
       AeET:FlET 86.53  8 < 0.0001
       AeET:ReET 86.54  8 < 0.0001
    CBT:McT:MiET 84.76  7 < 0.0001

Q statistic to assess consistency under the assumption of
a full design-by-treatment interaction random effects model

                    Q df p-value tau.within tau2.within
Between designs 17.84  9  0.0371     9.2598     85.7442

Code

# Node-splitting analysis to detect inconsistency
split_sr2 <- netsplit(nma_sr2)
render_netsplit_table(split_sr2)

Table 18: Node-splitting analysis for Subnet 2

comparison	TE	seTE	lower	upper	statistic	p
AeET:AqET	2.0920	5.5066	-8.7007	12.8847	0.3799	0.7040
AeET:CBT	2.0878	7.3376	-12.2937	16.4693	0.2845	0.7760
AeET:FlET	-8.6331	7.4095	-23.1554	5.8893	-1.1651	0.2440
AeET:McT	8.0436	6.3447	-4.3918	20.4790	1.2678	0.2049
AeET:MiET	11.0881	5.5488	0.2126	21.9636	1.9983	0.0457
AeET:ReET	0.8024	6.9163	-12.7533	14.3582	0.1160	0.9076
AeET:WlNi	-11.8420	4.3104	-20.2902	-3.3937	-2.7473	0.0060
AqET:CBT	-0.0042	8.2455	-16.1651	16.1568	-0.0005	0.9996
AqET:FlET	-10.7251	8.7720	-27.9178	6.4676	-1.2227	0.2215
AqET:McT	5.9516	7.2581	-8.2740	20.1772	0.8200	0.4122
AqET:MiET	8.9961	6.8076	-4.3465	22.3387	1.3215	0.1863
AqET:ReET	-1.2896	8.3969	-17.7473	15.1681	-0.1536	0.8779
AqET:WlNi	-13.9340	4.9170	-23.5710	-4.2969	-2.8339	0.0046
CBT:FlET	-10.7209	7.9843	-26.3698	4.9280	-1.3427	0.1794
CBT:McT	5.9558	7.4947	-8.7336	20.6451	0.7947	0.4268
CBT:MiET	9.0003	7.2413	-5.1924	23.1930	1.2429	0.2139
CBT:ReET	-1.2854	9.0789	-19.0798	16.5090	-0.1416	0.8874
CBT:WlNi	-13.9298	6.9363	-27.5247	-0.3349	-2.0083	0.0446
FlET:McT	16.6767	8.8311	-0.6320	33.9853	1.8884	0.0590
FlET:MiET	19.7212	8.4358	3.1872	36.2551	2.3378	0.0194
FlET:ReET	9.4355	8.0790	-6.3990	25.2700	1.1679	0.2428
FlET:WlNi	-3.2089	7.8130	-18.5220	12.1042	-0.4107	0.6813
McT:MiET	3.0445	4.9394	-6.6365	12.7255	0.6164	0.5376
McT:ReET	-7.2412	8.7847	-24.4589	9.9766	-0.8243	0.4098
McT:WlNi	-19.8856	5.6512	-30.9618	-8.8093	-3.5188	0.0004
MiET:ReET	-10.2857	8.3366	-26.6252	6.0538	-1.2338	0.2173
MiET:WlNi	-22.9301	5.2128	-33.1470	-12.7131	-4.3988	0.0000
ReET:WlNi	-12.6444	7.4107	-27.1692	1.8804	-1.7062	0.0880

Code

# Forest plot of node-splitting
plot_netsplit_forest(split_sr2)

Figure 9: Forest plot of node-splitting for Subnet 2

Code

# Heatmap
netheat(nma_sr2)

Code

# Funnel plot
funnel(nma_sr2, order = "WlNi", )

2 NMA for binary outcomes

Note

Please: Select an outcome to proceed.

Code

# Read data
data_alg <- read_excel("data/Banco de Dados_Rstudio (1).xlsx", sheet = "A | Lg")

# Transform to contrast-based
pw_alg <- pairwise(
    treat = list(Treat1, Treat2, Treat3),
    event = list(dp1, dp2, dp3), # observed events in each arm
    n = list(nRT1, nRT2, nRT3), # total number of patients in each arm
    studlab = StudyID,
    data = data_alg,
    sm = "OR" # Medida de efeito (OR = Odds Ratio, pode ser "RR" ou "RD" se preferir)
)

# Check network connections
net_con <- netconnection(pw_alg)
net_con

Number of studies: k = 31
Number of pairwise comparisons: m = 35
Number of treatments: n = 14
Number of designs: d = 18
Number of networks: 3

Details on subnetworks: 
 subnetwork  k  m n
          1 26 30 8
          2  1  1 2
          3  4  4 4

There are three sub-networks:

Subnet 1:
- 26 studies
- 30 comparisons
- 8 treatments

Subnet 2:
- 1 study
- 1 comparison
- 2 treatments

Subnet 3:
- 4 studies
- 4 comparisons
- 4 treatments

Tip

There are three treatment sub-networks that do not connect.

Note

Please: Select the treatment sub-networks before proceeding.

Select the procedures performed

Important

The first subnet contains 26 studies, 30 comparisons and 8 treatments. This is the main analysis network for binary outcomes.

Code

# Filter data for subnet 1
sr1_alg <- tibble(
    treat1 = net_con$treat1,
    treat2 = net_con$treat2,
    subnet = net_con$subnet
) |>
    filter(subnet == 1)

# Display first rows of comparison table
sr1_alg |> head()

Code

# Unique treatments in subnet 1
unique_treat1_alg <- sr1_alg$treat1 |> unique()
unique_treat2_alg <- sr1_alg$treat2 |> unique()

# Combination of all unique treatments
trat_sr1_alg <- unique(c(unique_treat1_alg, unique_treat2_alg))
print(trat_sr1_alg)

[1] "AeET" "AqET" "ReET" "McT"  "CBT"  "MiET" "WlNi" "FlET"

Code

# Total number of treatments
length(trat_sr1_alg)

[1] 8

Code

# Filter pairwise object only for comparisons within subnet 1
pw_sr1_alg <- pw_alg %>%
    filter(treat1 %in% trat_sr1_alg & treat2 %in% trat_sr1_alg)

# Number of comparisons in subnet 1
nrow(pw_sr1_alg)

[1] 34

Code

# Number of unique studies
length(unique(pw_sr1_alg$studlab))

[1] 30

Code

# Fit NMA model for subnet 1
nma_sr1_alg <- netmeta(
    TE,
    seTE,
    treat1,
    treat2,
    studlab,
    data = pw_sr1_alg,
    n1 = n1,
    n2 = n2,
    random = TRUE,
    common = FALSE,
    ref = "WlNi" # Reference treatment (change if you want another control)
)

Comparisons not considered in network meta-analysis:
                studlab treat1 treat2 TE seTE
             Etnier2009   MiET   WlNi NA   NA
           Hakkinen2001   ReET   WlNi NA   NA
             Sanudo2015   MiET   WlNi NA   NA
 RodriguezDominguez2026    McT   MiET NA   NA

Code

# Basic model summary
print(nma_sr1_alg)

Number of studies: k = 26
Number of pairwise comparisons: m = 30
Number of observations: o = 1925
Number of treatments: n = 8
Number of designs: d = 14

Random effects model

Treatment estimate (other treatments vs 'WlNi'):
         OR            95% CI    z p-value
AeET 1.8903 [1.0918;  3.2727] 2.27  0.0230
AqET 1.5777 [0.7160;  3.4764] 1.13  0.2580
CBT  1.7576 [0.7874;  3.9231] 1.38  0.1686
FlET 4.4105 [1.4492; 13.4232] 2.61  0.0090
McT  1.2585 [0.5944;  2.6646] 0.60  0.5481
MiET 1.4524 [0.6782;  3.1105] 0.96  0.3368
ReET 2.0228 [0.8373;  4.8863] 1.57  0.1175
WlNi      .                 .    .       .

Quantifying heterogeneity / inconsistency:
tau^2 = 0.0581; tau = 0.2410; I^2 = 9.9% [0.0%; 44.3%]

Tests of heterogeneity (within designs) and inconsistency (between designs):
                    Q d.f. p-value
Total           23.30   21  0.3280
Within designs  14.85   12  0.2496
Between designs  8.45    9  0.4895

Details of network meta-analysis methods:
- Frequentist graph-theoretical approach
- DerSimonian-Laird estimator for tau^2
- Calculation of I^2 based on Q

Note

These three studies were excluded from the model because they reported a standard deviation of zero, which makes division by zero impossible.
- Etnier2009: MiET vs WlNi
- Hakkinen2001: ReET vs WlNi
- Sanudo2015: MiET vs WlNi

Code

# Network plot
netgraph(
    nma_sr1_alg,
    seq = "optimal",
    number.of.studies = TRUE,
    cex.points = n.trts,
    labels = paste0(trts, "\n(n=", round(n.trts), ")")
)

Network plot of treatments for Subnet 1 (A | Lg)

Code

# Detailed model summary
summary(nma_sr1_alg)

Original data (with adjusted standard errors for multi-arm studies):

                       treat1 treat2      TE   seTE seTE.adj narms multiarm
Acosta-Gallego2018       AeET   AqET  0.1452 0.5394   0.5908     2         
Andrade2019              AqET   WlNi  0.0000 0.8660   0.8989     2         
Assis2006                AeET   AqET  0.0000 0.7596   0.7969     2         
Baptista2012             AeET   WlNi  0.0000 1.4322   1.4524     2         
Kayo2012                 AeET   ReET -0.1780 0.5973   0.7838     3        *
Kayo2012                 AeET   WlNi  0.0000 0.6105   0.8144     3        *
Kayo2012                 ReET   WlNi  0.1780 0.5973   0.7838     3        *
Larsson2015               McT   MiET  0.3747 0.4479   0.5086     2         
Letieri2013              AqET   WlNi -1.6710 1.5689   1.5874     2         
Mannerkorpi2000           McT   WlNi  1.5731 0.8247   0.8592     2         
Mannerkorpi2004           McT   WlNi -0.1823 0.6849   0.7260     2         
Mengshoel1992            AeET   WlNi  1.5404 0.7918   0.8277     2         
Munguia-Izquierdo 2007   AqET   WlNi  1.6025 1.1148   1.1406     2         
Rooks2007                 CBT    McT  0.6440 0.4070   0.6257     3        *
Rooks2007                 CBT   MiET  0.6224 0.3550   0.5057     3        *
Rooks2007                 McT   MiET -0.0216 0.3615   0.5162     3        *
Sanudo2011               MiET   WlNi  1.2040 1.1995   1.2235     2         
Sanudo2010a              AeET   MiET -0.0572 0.7838   0.8200     2         
Sanudo2010b              AeET   WlNi  1.4917 1.1643   1.1890     2         
Schachter2003            AeET   WlNi  1.1454 0.5236   0.5764     2         
Tomas-Carus2008          AqET   WlNi  0.6931 1.2780   1.3005     2         
Valim2003                AeET   FlET -0.6444 0.5776   0.6259     2         
Valkeinen2008            MiET   WlNi  1.4491 1.6003   1.6184     2         
Wang2018                  McT   MiET -0.8398 0.4465   0.5074     2         
Williams2010              CBT   WlNi -0.7687 0.6425   0.6862     2         
Hernando-Garijo2021      AeET   WlNi  0.0000 0.8997   0.9315     2         
Saranya2022               CBT   FlET -1.3499 1.2599   1.2827     2         
Erol2025                  CBT   WlNi  2.0317 1.5307   1.5495     2         
Fernandes2024            FlET   ReET  1.1499 1.1767   1.2011     2         
PontesSilva2026          AeET   ReET -0.4616 0.6030   0.6494     2         

Number of treatment arms per study (by decreasing number of arms):
                       narms multiarm
Kayo2012                   3        *
Rooks2007                  3        *
Acosta-Gallego2018         2         
Andrade2019                2         
Assis2006                  2         
Baptista2012               2         
Larsson2015                2         
Letieri2013                2         
Mannerkorpi2000            2         
Mannerkorpi2004            2         
Mengshoel1992              2         
Munguia-Izquierdo 2007     2         
Sanudo2011                 2         
Sanudo2010a                2         
Sanudo2010b                2         
Schachter2003              2         
Tomas-Carus2008            2         
Valim2003                  2         
Valkeinen2008              2         
Wang2018                   2         
Williams2010               2         
Hernando-Garijo2021        2         
Saranya2022                2         
Erol2025                   2         
Fernandes2024              2         
PontesSilva2026            2         

Results (random effects model):

                       treat1 treat2     OR           95% CI
Acosta-Gallego2018       AeET   AqET 1.1982 [0.5648; 2.5417]
Andrade2019              AqET   WlNi 1.5777 [0.7160; 3.4764]
Assis2006                AeET   AqET 1.1982 [0.5648; 2.5417]
Baptista2012             AeET   WlNi 1.8903 [1.0918; 3.2727]
Kayo2012                 AeET   ReET 0.9345 [0.4190; 2.0841]
Kayo2012                 AeET   WlNi 1.8903 [1.0918; 3.2727]
Kayo2012                 ReET   WlNi 2.0228 [0.8373; 4.8863]
Larsson2015               McT   MiET 0.8665 [0.5175; 1.4507]
Letieri2013              AqET   WlNi 1.5777 [0.7160; 3.4764]
Mannerkorpi2000           McT   WlNi 1.2585 [0.5944; 2.6646]
Mannerkorpi2004           McT   WlNi 1.2585 [0.5944; 2.6646]
Mengshoel1992            AeET   WlNi 1.8903 [1.0918; 3.2727]
Munguia-Izquierdo 2007   AqET   WlNi 1.5777 [0.7160; 3.4764]
Rooks2007                 CBT    McT 1.3966 [0.6704; 2.9094]
Rooks2007                 CBT   MiET 1.2101 [0.5948; 2.4622]
Rooks2007                 McT   MiET 0.8665 [0.5175; 1.4507]
Sanudo2011               MiET   WlNi 1.4524 [0.6782; 3.1105]
Sanudo2010a              AeET   MiET 1.3015 [0.5555; 3.0491]
Sanudo2010b              AeET   WlNi 1.8903 [1.0918; 3.2727]
Schachter2003            AeET   WlNi 1.8903 [1.0918; 3.2727]
Tomas-Carus2008          AqET   WlNi 1.5777 [0.7160; 3.4764]
Valim2003                AeET   FlET 0.4286 [0.1544; 1.1897]
Valkeinen2008            MiET   WlNi 1.4524 [0.6782; 3.1105]
Wang2018                  McT   MiET 0.8665 [0.5175; 1.4507]
Williams2010              CBT   WlNi 1.7576 [0.7874; 3.9231]
Hernando-Garijo2021      AeET   WlNi 1.8903 [1.0918; 3.2727]
Saranya2022               CBT   FlET 0.3985 [0.1136; 1.3978]
Erol2025                  CBT   WlNi 1.7576 [0.7874; 3.9231]
Fernandes2024            FlET   ReET 2.1805 [0.6595; 7.2092]
PontesSilva2026          AeET   ReET 0.9345 [0.4190; 2.0841]

Number of studies: k = 26
Number of pairwise comparisons: m = 30
Number of observations: o = 1925
Number of treatments: n = 8
Number of designs: d = 14

Random effects model

Treatment estimate (other treatments vs 'WlNi'):
         OR            95% CI    z p-value
AeET 1.8903 [1.0918;  3.2727] 2.27  0.0230
AqET 1.5777 [0.7160;  3.4764] 1.13  0.2580
CBT  1.7576 [0.7874;  3.9231] 1.38  0.1686
FlET 4.4105 [1.4492; 13.4232] 2.61  0.0090
McT  1.2585 [0.5944;  2.6646] 0.60  0.5481
MiET 1.4524 [0.6782;  3.1105] 0.96  0.3368
ReET 2.0228 [0.8373;  4.8863] 1.57  0.1175
WlNi      .                 .    .       .

Quantifying heterogeneity / inconsistency:
tau^2 = 0.0581; tau = 0.2410; I^2 = 9.9% [0.0%; 44.3%]

Tests of heterogeneity (within designs) and inconsistency (between designs):
                    Q d.f. p-value
Total           23.30   21  0.3280
Within designs  14.85   12  0.2496
Between designs  8.45    9  0.4895

Details of network meta-analysis methods:
- Frequentist graph-theoretical approach
- DerSimonian-Laird estimator for tau^2
- Calculation of I^2 based on Q

Code

# Forest plot
forest(nma_sr1_alg)

Forest plot of treatment effects for Subnet 1 (A | Lg)

Code

# Pairwise comparison table with confidence intervals
render_league_table(nma_sr1_alg)

Table 19: League table (random effects model) for Subnet 1 (A | Lg)

Comparison	AeET	AqET	CBT	FlET	McT	MiET	ReET	WlNi
AeET	AeET	1.0982 [0.4332; 2.7839]	.	0.5250 [0.1540; 1.7902]	.	0.9444 [0.1893; 4.7119]	0.7271 [0.2967; 1.7818]	2.0957 [1.0953; 4.0096]
AqET	1.1982 [0.5648; 2.5417]	AqET	.	.	.	.	.	1.3865 [0.4467; 4.3036]
CBT	1.0755 [0.4353; 2.6573]	0.8976 [0.3012; 2.6752]	CBT	0.2593 [0.0210; 3.2033]	1.9041 [0.7535; 4.8120]	1.8634 [0.8036; 4.3209]	.	0.7338 [0.2145; 2.5099]
FlET	0.4286 [0.1544; 1.1897]	0.3577 [0.1024; 1.2501]	0.3985 [0.1136; 1.3978]	FlET	.	.	3.1579 [0.2999; 33.2513]	.
McT	1.5021 [0.6351; 3.5529]	1.2537 [0.4364; 3.6014]	1.3966 [0.6704; 2.9094]	3.5047 [0.9906; 12.3998]	McT	0.8626 [0.5014; 1.4842]	.	1.7314 [0.5839; 5.1339]
MiET	1.3015 [0.5555; 3.0491]	1.0862 [0.3786; 3.1162]	1.2101 [0.5948; 2.4622]	3.0367 [0.8643; 10.6689]	0.8665 [0.5175; 1.4507]	MiET	.	3.6441 [0.5381; 24.6805]
ReET	0.9345 [0.4190; 2.0841]	0.7800 [0.2679; 2.2705]	0.8689 [0.2769; 2.7268]	2.1805 [0.6595; 7.2092]	0.6221 [0.2043; 1.8949]	0.7180 [0.2366; 2.1792]	ReET	1.1948 [0.3381; 4.2224]
WlNi	1.8903 [1.0918; 3.2727]	1.5777 [0.7160; 3.4764]	1.7576 [0.7874; 3.9231]	4.4105 [1.4492; 13.4232]	1.2585 [0.5944; 2.6646]	1.4524 [0.6782; 3.1105]	2.0228 [0.8373; 4.8863]	WlNi

Code

# Treatment ranking
ranking_sr1_alg <- netrank(nma_sr1_alg, small.values = "good")
print(ranking_sr1_alg)

     P-score
WlNi  0.8956
McT   0.7219
MiET  0.5900
AqET  0.5408
CBT   0.4393
AeET  0.3901
ReET  0.3718
FlET  0.0505

Code

# Rankogram
ran_sr1_alg <- rankogram(nma_sr1_alg)
plot(ran_sr1_alg)

Rankogram of treatments for Subnet 1 (A | Lg)

Code

# Design decomposition
decomp_sr1_alg <- decomp.design(nma_sr1_alg)
print(decomp_sr1_alg)

Q statistics to assess homogeneity / consistency

                    Q df p-value
Total           23.30 21  0.3280
Within designs  14.85 12  0.2496
Between designs  8.45  9  0.4895

Design-specific decomposition of within-designs Q statistic

    Design    Q df p-value
  McT:MiET 3.69  1  0.0548
  WlNi:CBT 2.85  1  0.0916
  WlNi:McT 2.68  1  0.1015
 WlNi:AqET 3.16  3  0.3683
 WlNi:AeET 2.44  4  0.6547
 AeET:AqET 0.02  1  0.8762
 WlNi:MiET 0.02  1  0.9025

Between-designs Q statistic after detaching of single designs
(influential designs have p-value markedly different from 0.4895)

 Detached design    Q df p-value
    CBT:McT:MiET 3.33  7  0.8534
        WlNi:CBT 4.29  8  0.8298
       WlNi:AeET 6.19  8  0.6257
       WlNi:MiET 7.24  8  0.5109
        WlNi:McT 7.63  8  0.4702
  WlNi:AeET:ReET 6.63  7  0.4689
       AeET:ReET 7.75  8  0.4584
       AeET:FlET 8.07  8  0.4265
       AeET:MiET 8.13  8  0.4207
        McT:MiET 8.22  8  0.4125
        CBT:FlET 8.28  8  0.4068
       FlET:ReET 8.31  8  0.4040
       AeET:AqET 8.35  8  0.4002
       WlNi:AqET 8.35  8  0.4002

Q statistic to assess consistency under the assumption of
a full design-by-treatment interaction random effects model

                   Q df p-value tau.within tau2.within
Between designs 5.56  9  0.7831     0.4000      0.1600

Code

# Node-splitting analysis to detect inconsistency
split_sr1_alg <- netsplit(nma_sr1_alg) # , show = "all")
render_netsplit_table(split_sr1_alg)

Table 20: Node-splitting analysis for Subnet 1 (A | Lg)

comparison	TE	seTE	lower	upper	statistic	p
AeET:AqET	0.1808	0.3837	-0.5713	0.9328	0.4712	0.6375
AeET:CBT	0.0728	0.4615	-0.8318	0.9773	0.1577	0.8747
AeET:FlET	-0.8473	0.5209	-1.8682	0.1737	-1.6265	0.1038
AeET:McT	0.4069	0.4392	-0.4541	1.2678	0.9263	0.3543
AeET:MiET	0.2635	0.4344	-0.5878	1.1148	0.6067	0.5441
AeET:ReET	-0.0677	0.4092	-0.8698	0.7343	-0.1655	0.8686
AeET:WlNi	0.6367	0.2800	0.0879	1.1856	2.2737	0.0230
AqET:CBT	-0.1080	0.5572	-1.2001	0.9840	-0.1939	0.8463
AqET:FlET	-1.0281	0.6384	-2.2793	0.2232	-1.6103	0.1073
AqET:McT	0.2261	0.5384	-0.8292	1.2813	0.4199	0.6746
AqET:MiET	0.0827	0.5377	-0.9712	1.1366	0.1538	0.8777
AqET:ReET	-0.2485	0.5452	-1.3170	0.8200	-0.4558	0.6485
AqET:WlNi	0.4559	0.4031	-0.3341	1.2460	1.1311	0.2580
CBT:FlET	-0.9200	0.6403	-2.1750	0.3349	-1.4369	0.1507
CBT:McT	0.3341	0.3744	-0.3998	1.0680	0.8922	0.3723
CBT:MiET	0.1907	0.3624	-0.5196	0.9010	0.5263	0.5987
CBT:ReET	-0.1405	0.5835	-1.2841	1.0031	-0.2408	0.8097
CBT:WlNi	0.5640	0.4097	-0.2390	1.3669	1.3766	0.1686
FlET:McT	1.2541	0.6447	-0.0095	2.5177	1.9453	0.0517
FlET:MiET	1.1108	0.6411	-0.1458	2.3673	1.7326	0.0832
FlET:ReET	0.7795	0.6101	-0.4163	1.9754	1.2777	0.2014
FlET:WlNi	1.4840	0.5679	0.3710	2.5970	2.6133	0.0090
McT:MiET	-0.1433	0.2629	-0.6587	0.3720	-0.5452	0.5856
McT:ReET	-0.4746	0.5682	-1.5883	0.6392	-0.8352	0.4036
McT:WlNi	0.2299	0.3827	-0.5203	0.9800	0.6006	0.5481
MiET:ReET	-0.3312	0.5664	-1.4414	0.7790	-0.5848	0.5587
MiET:WlNi	0.3732	0.3885	-0.3883	1.1348	0.9606	0.3368
ReET:WlNi	0.7045	0.4500	-0.1775	1.5864	1.5655	0.1175

Code

# Forest plot of node-splitting
plot_netsplit_forest(split_sr1_alg)

Figure 10: Forest plot of node-splitting for Subnet 1 (A | Lg)

Code

# Heatmap
netheat(nma_sr1_alg)

Heatmap of contribution matrix for Subnet 1 (A | Lg)

Code

# Funnel plot
funnel(nma_sr1_alg,
    order = "WlNi",
)

Select the procedures performed

Important

The second subnet contains only 1 study with 1 comparison between 2 treatments. Due to its limited size, the analysis will be simplified.

Code

# Filter data for subnet 2
sr2_alg <- tibble(
    treat1 = net_con$treat1,
    treat2 = net_con$treat2,
    subnet = net_con$subnet
) |>
    filter(subnet == 2)

# Display comparison table
sr2_alg

Code

# Unique treatments in subnet 2
unique_treat1_sr2_alg <- sr2_alg$treat1 |> unique()
unique_treat2_sr2_alg <- sr2_alg$treat2 |> unique()

# Combination of all unique treatments
trat_sr2_alg <- unique(c(unique_treat1_sr2_alg, unique_treat2_sr2_alg))
print(trat_sr2_alg)

[1] "DryN" "MasT"

Code

# Total number of treatments
length(trat_sr2_alg)

[1] 2

Code

# Filter pairwise object only for comparisons within subnet 2
pw_sr2_alg <- pw_alg %>%
    filter(treat1 %in% trat_sr2_alg & treat2 %in% trat_sr2_alg)

# Number of comparisons in subnet 2
nrow(pw_sr2_alg)

[1] 1

Code

# Number of unique studies
length(unique(pw_sr2_alg$studlab))

[1] 1

Code

# Fit NMA model for subnet 2
nma_sr2_alg <- netmeta(
    TE, seTE, treat1, treat2, studlab,
    data = pw_sr2_alg,
    n1 = n1,
    n2 = n2,
    random = TRUE,
    common = FALSE,
    ref = "MasT" # First treatment as reference
)

# Basic model summary
print(nma_sr2_alg)

Number of studies: k = 1
Number of pairwise comparisons: m = 1
Number of observations: o = 64
Number of treatments: n = 2
Number of designs: d = 1

Random effects model

Treatment estimate ('DryN' vs 'MasT'):
         OR           95% CI     z p-value
DryN 0.6444 [0.1003; 4.1421] -0.46  0.6435
MasT      .                .     .       .

Quantifying heterogeneity:
tau^2 = NA; tau = NA

Details of network meta-analysis methods:
- Frequentist graph-theoretical approach
- DerSimonian-Laird estimator for tau^2

Code

# Network plot
netgraph(
    nma_sr2_alg,
    # seq = "optimal",
    number.of.studies = TRUE,
    cex.points = n.trts,
    labels = paste0(trts, "\n(n=", round(n.trts), ")")
)

Network plot of treatments for Subnet 2 (A | Lg)

Code

# Detailed model summary
summary(nma_sr2_alg)

Original data:

                   treat1 treat2      TE   seTE
Castro-Sanchez2011   DryN   MasT -0.4394 0.9493

Number of treatment arms per study:
                   narms
Castro-Sanchez2011     2

Results (random effects model):

                   treat1 treat2     OR           95% CI
Castro-Sanchez2011   DryN   MasT 0.6444 [0.1003; 4.1421]

Number of studies: k = 1
Number of pairwise comparisons: m = 1
Number of observations: o = 64
Number of treatments: n = 2
Number of designs: d = 1

Random effects model

Treatment estimate ('DryN' vs 'MasT'):
         OR           95% CI     z p-value
DryN 0.6444 [0.1003; 4.1421] -0.46  0.6435
MasT      .                .     .       .

Quantifying heterogeneity:
tau^2 = NA; tau = NA

Details of network meta-analysis methods:
- Frequentist graph-theoretical approach
- DerSimonian-Laird estimator for tau^2

Select the procedures performed

Important

The third subnet contains 4 studies, 4 comparisons and 4 treatments.

Code

# Filter data for subnet 3
sr3_alg <- tibble(
    treat1 = net_con$treat1,
    treat2 = net_con$treat2,
    subnet = net_con$subnet
) |>
    filter(subnet == 3)

# Display comparison table
sr3_alg

Code

# Unique treatments in subnet 3
unique_treat1_sr3_alg <- sr3_alg$treat1 |> unique()
unique_treat2_sr3_alg <- sr3_alg$treat2 |> unique()

# Combination of all unique treatments
trat_sr3_alg <- unique(c(unique_treat1_sr3_alg, unique_treat2_sr3_alg))
print(trat_sr3_alg)

[1] "MfT"   "Acu"   "rTMS"  "PlaSh"

Code

# Total number of treatments
length(trat_sr3_alg)

[1] 4

Code

# Filter pairwise object only for comparisons within subnet 3
pw_sr3_alg <- pw_alg %>%
    filter(treat1 %in% trat_sr3_alg & treat2 %in% trat_sr3_alg)

# Number of comparisons in subnet 3
nrow(pw_sr3_alg)

[1] 5

Code

# Number of unique studies
length(unique(pw_sr3_alg$studlab))

[1] 5

Code

# Fit NMA model for subnet 3
nma_sr3_alg <- netmeta(
    TE, seTE, treat1, treat2, studlab,
    data = pw_sr3_alg,
    n1 = n1,
    n2 = n2,
    random = TRUE,
    common = FALSE,
    ref = "PlaSh" # Reference treatment (change if you want another control)
)

Comparison not considered in network meta-analysis:
   studlab treat1 treat2 TE seTE
 Silva2025   rTMS  PlaSh NA   NA

Code

# Basic model summary
print(nma_sr3_alg)

Number of studies: k = 4
Number of pairwise comparisons: m = 4
Number of observations: o = 295
Number of treatments: n = 4
Number of designs: d = 3

Random effects model

Treatment estimate (other treatments vs 'PlaSh'):
          OR           95% CI     z p-value
Acu   0.7683 [0.3521; 1.6766] -0.66  0.5079
MfT   1.0559 [0.4113; 2.7111]  0.11  0.9100
PlaSh      .                .     .       .
rTMS  0.5833 [0.1362; 2.4976] -0.73  0.4676

Quantifying heterogeneity / inconsistency:
tau^2 = 0; tau = 0; I^2 = 0%

Tests of heterogeneity (within designs) and inconsistency (between designs):
                   Q d.f. p-value
Total           0.38    1  0.5363
Within designs  0.38    1  0.5363
Between designs 0.00    0      --

Details of network meta-analysis methods:
- Frequentist graph-theoretical approach
- DerSimonian-Laird estimator for tau^2
- Calculation of I^2 based on Q

Code

# Network plot
netgraph(
    nma_sr3_alg,
    seq = "optimal",
    number.of.studies = TRUE,
    cex.points = n.trts,
    labels = paste0(trts, "\n(n=", round(n.trts), ")")
)

Network plot of treatments for Subnet 3 (A | Lg)

Code

# Detailed model summary
summary(nma_sr3_alg)

Original data:

            treat1 treat2      TE   seTE
Alfano2001     MfT  PlaSh  0.2184 0.5493
Colbert1999    MfT  PlaSh -0.4855 0.9968
Harris2005     Acu  PlaSh -0.2636 0.3981
Mhalla2011   PlaSh   rTMS  0.5390 0.7420

Number of treatment arms per study:
            narms
Alfano2001      2
Colbert1999     2
Harris2005      2
Mhalla2011      2

Results (random effects model):

            treat1 treat2     OR           95% CI
Alfano2001     MfT  PlaSh 1.0559 [0.4113; 2.7111]
Colbert1999    MfT  PlaSh 1.0559 [0.4113; 2.7111]
Harris2005     Acu  PlaSh 0.7683 [0.3521; 1.6766]
Mhalla2011   PlaSh   rTMS 1.7143 [0.4004; 7.3399]

Number of studies: k = 4
Number of pairwise comparisons: m = 4
Number of observations: o = 295
Number of treatments: n = 4
Number of designs: d = 3

Random effects model

Treatment estimate (other treatments vs 'PlaSh'):
          OR           95% CI     z p-value
Acu   0.7683 [0.3521; 1.6766] -0.66  0.5079
MfT   1.0559 [0.4113; 2.7111]  0.11  0.9100
PlaSh      .                .     .       .
rTMS  0.5833 [0.1362; 2.4976] -0.73  0.4676

Quantifying heterogeneity / inconsistency:
tau^2 = 0; tau = 0; I^2 = 0%

Tests of heterogeneity (within designs) and inconsistency (between designs):
                   Q d.f. p-value
Total           0.38    1  0.5363
Within designs  0.38    1  0.5363
Between designs 0.00    0      --

Details of network meta-analysis methods:
- Frequentist graph-theoretical approach
- DerSimonian-Laird estimator for tau^2
- Calculation of I^2 based on Q

Code

# Forest plot
forest(nma_sr3_alg)

Forest plot of treatment effects for Subnet 3 (A | Lg)

Code

# Pairwise comparison table with confidence intervals
render_league_table(nma_sr3_alg)

Table 21: League table (random effects model) for Subnet 3 (A | Lg)

Comparison	Acu	MfT	PlaSh	rTMS
Acu	Acu	.	0.7683 [0.3521; 1.6766]	.
MfT	0.7276 [0.2140; 2.4743]	MfT	1.0559 [0.4113; 2.7111]	.
PlaSh	0.7683 [0.3521; 1.6766]	1.0559 [0.4113; 2.7111]	PlaSh	1.7143 [0.4004; 7.3399]
rTMS	1.3171 [0.2528; 6.8611]	1.8101 [0.3199; 10.2437]	1.7143 [0.4004; 7.3399]	rTMS

Code

# Treatment ranking
ranking_sr3_alg <- netrank(nma_sr3_alg, small.values = "good")
print(ranking_sr3_alg)

      P-score
rTMS   0.7144
Acu    0.6042
PlaSh  0.3443
MfT    0.3371

Code

# Rankogram
ran_sr3_alg <- rankogram(nma_sr3_alg)
plot(ran_sr3_alg)

Rankogram of treatments for Subnet 3 (A | Lg)

Code

# Design decomposition
decomp_sr3_alg <- decomp.design(nma_sr3_alg)
print(decomp_sr3_alg)

Q statistics to assess homogeneity / consistency

                   Q df p-value
Total           0.38  1  0.5363
Within designs  0.38  1  0.5363
Between designs 0.00  0      --

Design-specific decomposition of within-designs Q statistic

    Design    Q df p-value
 PlaSh:MfT 0.38  1  0.5363

Q statistic to assess consistency under the assumption of
a full design-by-treatment interaction random effects model

                   Q df p-value tau.within tau2.within
Between designs 0.00  0      --          0           0

Code

# Node-splitting analysis to detect inconsistency
split_sr3_alg <- netsplit(nma_sr3_alg, show = "all")
render_netsplit_table(split_sr3_alg)

Table 22: Node-splitting analysis for Subnet 3 (A | Lg)

comparison	TE	seTE	lower	upper	statistic	p
Acu:MfT	-0.3180	0.6245	-1.5419	0.9060	-0.5092	0.6106
Acu:PlaSh	-0.2636	0.3981	-1.0439	0.5168	-0.6620	0.5079
Acu:rTMS	0.2754	0.8421	-1.3751	1.9259	0.3271	0.7436
MfT:PlaSh	0.0544	0.4811	-0.8885	0.9973	0.1131	0.9100
MfT:rTMS	0.5934	0.8843	-1.1399	2.3267	0.6710	0.5022
rTMS:PlaSh	-0.5390	0.7420	-1.9933	0.9153	-0.7264	0.4676

Code

# Forest plot of node-splitting
plot_netsplit_forest(split_sr3_alg)

Figure 11: Forest plot of node-splitting for Subnet 3 (A | Lg)

Code

# Heatmap
netheat(nma_sr3_alg)

Code

# Funnel plot
funnel(nma_sr3_alg,
    order = "PlaSh",
)

Code

# Read data
data_aim <- read_excel("data/Banco de Dados_Rstudio (1).xlsx", sheet = "A | Im")|>
    mutate(
        nRT1 = as.numeric(nRT1),
        nRT2 = as.numeric(nRT2),
        dp1 = as.numeric(dp1),
        dp2 = as.numeric(dp2)
    ) |>
    tidyr::drop_na() # excliniodo as linhas com NA


# Transform to contrast-based
pw_aim <- pairwise(
    treat = list(Treat1, Treat2),
    event = list(dp1, dp2), # observed events in each arm
    n = list(nRT1, nRT2), # total number of patients in each arm
    studlab = StudyID,
    data = data_aim,
    sm = "OR" # Medida de efeito (OR = Odds Ratio, pode ser "RR" ou "RD" se preferir)
)

# Check network connections
net_con <- netconnection(pw_aim)
net_con

Number of studies: k = 14
Number of pairwise comparisons: m = 14
Number of treatments: n = 8
Number of designs: d = 7
Number of networks: 1

There are three sub-networks:

Network:
- 13 studies
- 13 comparisons
- 8 treatments

Tip

The network is fully connected.

2.0.1 Network

Select the procedures performed

Important

The Network contains 13 studies, 13 comparisons and 8 treatments. This is the main analysis network for binary outcomes.

Code

# Filter data for subnet 1
sr1_aim <- tibble(
    treat1 = net_con$treat1,
    treat2 = net_con$treat2,
    subnet = net_con$subnet
) |>
    filter(subnet == 1)

# Display first rows of comparison table
sr1_aim |> head()

Code

# Unique treatments in subnet 1
unique_treat1_aim <- sr1_aim$treat1 |> unique()
unique_treat2_aim <- sr1_aim$treat2 |> unique()

# Combination of all unique treatments
trat_sr_aim <- unique(c(unique_treat1_aim, unique_treat2_aim))
print(trat_sr_aim)

[1] "Acu"   "rTMS"  "tDCS"  "McT"   "Elec"  "Bal"   "PlaSh" "WlNi"

Code

# Total number of treatments
length(trat_sr_aim)

[1] 8

Code

# Filter pairwise object only for comparisons within network
pw_sr_aim <- pw_aim %>%
    filter(treat1 %in% trat_sr_aim & treat2 %in% trat_sr_aim)

# Number of comparisons in network
nrow(pw_sr_aim)

[1] 28

Code

# Number of unique studies
length(unique(pw_sr_aim$studlab))

[1] 28

Code

# Fit NMA model for subnet 1
nma_sr_aim <- netmeta(
    TE, seTE, treat1, treat2, studlab,
    data = pw_sr_aim,
    n1 = n1,
    n2 = n2,
    random = TRUE,
    common = FALSE,
    ref = "WlNi" # Reference treatment (change if you want another control)
)

Comparisons not considered in network meta-analysis:
        studlab treat1 treat2 TE seTE
      Boyer2014   rTMS  PlaSh NA   NA
   Brietzke2019   tDCS  PlaSh NA   NA
    Buskila2001    Bal   WlNi NA   NA
   Curatolo2017   tDCS  PlaSh NA   NA
 Fioravanti2007    Bal   WlNi NA   NA
   Mendonça2011   tDCS  PlaSh NA   NA
    Neumann2001    Bal   WlNi NA   NA
    Passard2007   rTMS  PlaSh NA   NA
      Short2011   rTMS  PlaSh NA   NA
     Stival2013    Acu  PlaSh NA   NA
      Valle2009   tDCS  PlaSh NA   NA
     Forogh2021   rTMS   tDCS NA   NA
      Caumo2023   tDCS  PlaSh NA   NA
   Betancur2025   tDCS  PlaSh NA   NA

Code

# Basic model summary
print(nma_sr_aim)

Number of studies: k = 14
Number of pairwise comparisons: m = 14
Number of observations: o = 698
Number of treatments: n = 8
Number of designs: d = 7

Random effects model

Treatment estimate (other treatments vs 'WlNi'):
          OR            95% CI     z p-value
Acu   0.1924 [0.0065;  5.6813] -0.95  0.3400
Bal   4.5714 [0.4731; 44.1699]  1.31  0.1891
Elec  0.0425 [0.0006;  3.0969] -1.44  0.1489
McT   1.4655 [0.6439;  3.3355]  0.91  0.3624
PlaSh 0.7509 [0.0368; 15.3360] -0.19  0.8523
rTMS  0.6994 [0.0275; 17.8012] -0.22  0.8286
tDCS  1.0000 [0.0591; 16.9279]  0.00  1.0000
WlNi       .                 .     .       .

Quantifying heterogeneity / inconsistency:
tau^2 = 0; tau = 0; I^2 = 0% [0.0%; 67.6%]

Tests of heterogeneity (within designs) and inconsistency (between designs):
                   Q d.f. p-value
Total           1.26    7  0.9895
Within designs  1.26    7  0.9895
Between designs 0.00    0      --

Details of network meta-analysis methods:
- Frequentist graph-theoretical approach
- DerSimonian-Laird estimator for tau^2
- Calculation of I^2 based on Q

Note

These three studies were excluded from the model because they reported a standard deviation of zero, which makes division by zero impossible.
- Boyer2014: rTMS vs PlaSh
- Brietzke2019: tDCS vs PlaSh
- Buskila2001: Bal vs WlNi
- Curatolo2017: tDCS vs PlaSh
- Fioravanti2007: Bal vs WlNi
- Mendonça2011: tDCS vs PlaSh
- Neumann2001: Bal vs WlNi
- Passard2007: rTMS vs PlaSh
- Short2011: rTMS vs PlaSh
- Stival2013: Acu vs PlaSh
- Valle2009: tDCS vs PlaSh
- Forogh2021: rTMS vs tDCS

Code

# Network plot
netgraph(
    nma_sr_aim,
    seq = "optimal",
    number.of.studies = TRUE,
    cex.points = n.trts,
    labels = paste0(trts, "\n(n=", round(n.trts), ")")
)

Network plot of treatments for Subnet 1 (A | Im)

Code

# Detailed model summary
summary(nma_sr_aim)

Original data:

              treat1 treat2      TE   seTE
Assefi2005       Acu  PlaSh -1.3614 0.7836
Cheng2019      PlaSh   rTMS  0.9985 1.6933
Fagerlund2015   tDCS   WlNi  0.0000 1.4434
Fregni2006     PlaSh   tDCS -0.3819 1.6758
Hamnes2012       McT   WlNi  0.3822 0.4196
Khedr2017      PlaSh   tDCS -0.0000 1.0541
Lauretti2013    Elec  PlaSh -2.8717 1.5552
Lee2012        PlaSh   rTMS -0.2231 1.0000
Mhalla2011     PlaSh   rTMS -0.0000 1.4510
Ozkurt2011       Bal   WlNi  1.5198 1.1573
Tekin2014      PlaSh   rTMS  1.2141 1.6565
Yagci2014      PlaSh   rTMS -0.6131 1.2885
deMelo2020     PlaSh   tDCS -0.3185 0.9170
Loreti2023     PlaSh   tDCS -0.4626 0.9735

Number of treatment arms per study:
              narms
Assefi2005        2
Cheng2019         2
Fagerlund2015     2
Fregni2006        2
Hamnes2012        2
Khedr2017         2
Lauretti2013      2
Lee2012           2
Mhalla2011        2
Ozkurt2011        2
Tekin2014         2
Yagci2014         2
deMelo2020        2
Loreti2023        2

Results (random effects model):

              treat1 treat2     OR            95% CI
Assefi2005       Acu  PlaSh 0.2563 [0.0552;  1.1904]
Cheng2019      PlaSh   rTMS 1.0737 [0.3321;  3.4707]
Fagerlund2015   tDCS   WlNi 1.0000 [0.0591; 16.9279]
Fregni2006     PlaSh   tDCS 0.7509 [0.2634;  2.1406]
Hamnes2012       McT   WlNi 1.4655 [0.6439;  3.3355]
Khedr2017      PlaSh   tDCS 0.7509 [0.2634;  2.1406]
Lauretti2013    Elec  PlaSh 0.0566 [0.0027;  1.1930]
Lee2012        PlaSh   rTMS 1.0737 [0.3321;  3.4707]
Mhalla2011     PlaSh   rTMS 1.0737 [0.3321;  3.4707]
Ozkurt2011       Bal   WlNi 4.5714 [0.4731; 44.1699]
Tekin2014      PlaSh   rTMS 1.0737 [0.3321;  3.4707]
Yagci2014      PlaSh   rTMS 1.0737 [0.3321;  3.4707]
deMelo2020     PlaSh   tDCS 0.7509 [0.2634;  2.1406]
Loreti2023     PlaSh   tDCS 0.7509 [0.2634;  2.1406]

Number of studies: k = 14
Number of pairwise comparisons: m = 14
Number of observations: o = 698
Number of treatments: n = 8
Number of designs: d = 7

Random effects model

Treatment estimate (other treatments vs 'WlNi'):
          OR            95% CI     z p-value
Acu   0.1924 [0.0065;  5.6813] -0.95  0.3400
Bal   4.5714 [0.4731; 44.1699]  1.31  0.1891
Elec  0.0425 [0.0006;  3.0969] -1.44  0.1489
McT   1.4655 [0.6439;  3.3355]  0.91  0.3624
PlaSh 0.7509 [0.0368; 15.3360] -0.19  0.8523
rTMS  0.6994 [0.0275; 17.8012] -0.22  0.8286
tDCS  1.0000 [0.0591; 16.9279]  0.00  1.0000
WlNi       .                 .     .       .

Quantifying heterogeneity / inconsistency:
tau^2 = 0; tau = 0; I^2 = 0% [0.0%; 67.6%]

Tests of heterogeneity (within designs) and inconsistency (between designs):
                   Q d.f. p-value
Total           1.26    7  0.9895
Within designs  1.26    7  0.9895
Between designs 0.00    0      --

Details of network meta-analysis methods:
- Frequentist graph-theoretical approach
- DerSimonian-Laird estimator for tau^2
- Calculation of I^2 based on Q

Code

# Forest plot
forest(nma_sr_aim)

Forest plot of treatment effects for Subnet 1 (A | Im)

Code

# Pairwise comparison table with confidence intervals
render_league_table(nma_sr_aim)

Table 23: League table (random effects model) for Subnet 1 (A | Im)

Comparison	Acu	Bal	Elec	McT	PlaSh	rTMS	tDCS	WlNi
Acu	Acu	.	.	.	0.2563 [0.0552; 1.1904]	.	.	.
Bal	0.0421 [0.0007; 2.4769]	Bal	.	.	.	.	.	4.5714 [0.4731; 44.1699]
Elec	4.5278 [0.1491; 137.4731]	107.5544 [0.8408; 13758.9798]	Elec	.	0.0566 [0.0027; 1.1930]	.	.	.
McT	0.1313 [0.0040; 4.2778]	3.1193 [0.2794; 34.8246]	0.0290 [0.0004; 2.2849]	McT	.	.	.	1.4655 [0.6439; 3.3355]
PlaSh	0.2563 [0.0552; 1.1904]	6.0880 [0.1397; 265.2330]	0.0566 [0.0027; 1.1930]	1.9517 [0.0856; 44.5000]	PlaSh	1.0737 [0.3321; 3.4707]	0.7509 [0.2634; 2.1406]	.
rTMS	0.2752 [0.0398; 1.9008]	6.5364 [0.1255; 340.3042]	0.0608 [0.0023; 1.5929]	2.0955 [0.0743; 59.1125]	1.0737 [0.3321; 3.4707]	rTMS	.	.
tDCS	0.1924 [0.0300; 1.2350]	4.5714 [0.1217; 171.7126]	0.0425 [0.0017; 1.0671]	1.4655 [0.0770; 27.8905]	0.7509 [0.2634; 2.1406]	0.6994 [0.1451; 3.3714]	tDCS	1.0000 [0.0591; 16.9279]
WlNi	0.1924 [0.0065; 5.6813]	4.5714 [0.4731; 44.1699]	0.0425 [0.0006; 3.0969]	1.4655 [0.6439; 3.3355]	0.7509 [0.0368; 15.3360]	0.6994 [0.0275; 17.8012]	1.0000 [0.0591; 16.9279]	WlNi

Code

# Treatment ranking
ranking_sr_aim <- netrank(nma_sr_aim, small.values = "good")
print(ranking_sr_aim)

      P-score
Elec   0.9344
Acu    0.8078
rTMS   0.4912
WlNi   0.4727
PlaSh  0.4704
tDCS   0.3696
McT    0.3222
Bal    0.1316

Code

# Rankogram
ran_sr_aim <- rankogram(nma_sr_aim)
plot(ran_sr_aim)

Rankogram of treatments for Subnet 1 (A | Im)

Code

# Design decomposition
decomp_sr_aim <- decomp.design(nma_sr_aim)
print(decomp_sr_aim)

Q statistics to assess homogeneity / consistency

                   Q df p-value
Total           1.26  7  0.9895
Within designs  1.26  7  0.9895
Between designs 0.00  0      --

Design-specific decomposition of within-designs Q statistic

     Design    Q df p-value
 PlaSh:rTMS 1.15  4  0.8867
 PlaSh:tDCS 0.11  3  0.9905

Q statistic to assess consistency under the assumption of
a full design-by-treatment interaction random effects model

                   Q df p-value tau.within tau2.within
Between designs 0.00  0      --          0           0

Code

# Node-splitting analysis to detect inconsistency
split_sr_aim <- netsplit(nma_sr_aim, show = "all")
render_netsplit_table(split_sr_aim)

Table 24: Node-splitting analysis for Subnet 1 (A | Im)

comparison	TE	seTE	lower	upper	statistic	p
Acu:Bal	-3.1678	2.0790	-7.2425	0.9070	-1.5237	0.1276
Acu:Elec	1.5102	1.7415	-1.9029	4.9234	0.8672	0.3858
Acu:McT	-2.0301	1.7774	-5.5137	1.4534	-1.1422	0.2534
Acu:PlaSh	-1.3614	0.7836	-2.8972	0.1743	-1.7375	0.0823
Acu:rTMS	-1.2904	0.9861	-3.2230	0.6423	-1.3086	0.1907
Acu:tDCS	-1.6479	0.9485	-3.5069	0.2111	-1.7374	0.0823
Acu:WlNi	-1.6479	1.7271	-5.0330	1.7372	-0.9541	0.3400
Bal:Elec	4.6780	2.4753	-0.1735	9.5294	1.8899	0.0588
Bal:McT	1.1376	1.2310	-1.2751	3.5503	0.9241	0.3554
Bal:PlaSh	1.8063	1.9257	-1.9680	5.5806	0.9380	0.3482
Bal:rTMS	1.8774	2.0166	-2.0751	5.8298	0.9310	0.3519
Bal:tDCS	1.5198	1.8500	-2.1062	5.1458	0.8215	0.4114
Bal:WlNi	1.5198	1.1573	-0.7484	3.7880	1.3133	0.1891
Elec:McT	-3.5404	2.2280	-7.9071	0.8263	-1.5891	0.1120
Elec:PlaSh	-2.8717	1.5552	-5.9198	0.1765	-1.8465	0.0648
Elec:rTMS	-2.8006	1.6664	-6.0668	0.4656	-1.6806	0.0928
Elec:tDCS	-3.1582	1.6445	-6.3813	0.0650	-1.9204	0.0548
Elec:WlNi	-3.1582	2.1881	-7.4467	1.1304	-1.4434	0.1489
McT:PlaSh	0.6687	1.5953	-2.4581	3.7955	0.4192	0.6751
McT:rTMS	0.7398	1.7039	-2.5999	4.0794	0.4342	0.6642
McT:tDCS	0.3822	1.5031	-2.5639	3.3283	0.2543	0.7993
McT:WlNi	0.3822	0.4196	-0.4402	1.2046	0.9109	0.3624
PlaSh:rTMS	0.0711	0.5986	-1.1022	1.2444	0.1187	0.9055
PlaSh:tDCS	-0.2865	0.5345	-1.3341	0.7611	-0.5360	0.5920
PlaSh:WlNi	-0.2865	1.5392	-3.3032	2.7302	-0.1861	0.8523
rTMS:tDCS	-0.3576	0.8025	-1.9305	1.2153	-0.4456	0.6559
rTMS:WlNi	-0.3576	1.6515	-3.5944	2.8793	-0.2165	0.8286
tDCS:WlNi	0.0000	1.4434	-2.8290	2.8290	0.0000	1.0000

Code

# Forest plot of node-splitting
plot_netsplit_forest(split_sr_aim)

Figure 12: Forest plot of node-splitting for Subnet 1 (A | Im)

Code

# Heatmap
netheat(nma_sr_aim)

Code

# Funnel plot
funnel(nma_sr_aim,
    order = "WlNi",
)

Code

# Read data
data_ash <- read_excel("data/Banco de Dados_Rstudio (1).xlsx", sheet = "A | Sh")|>
    mutate(
        nRT1 = as.numeric(nRT1),
        nRT2 = as.numeric(nRT2),
        dp1 = as.numeric(dp1),
        dp2 = as.numeric(dp2)
    ) 


# Transform to contrast-based
pw_ash <- pairwise(
    treat = list(Treat1, Treat2, Treat3),
    event = list(dp1, dp2, dp3), # observed events in each arm
    n = list(nRT1, nRT2, nRT3), # total number of patients in each arm
    studlab = StudyID,
    data = data_ash,
    sm = "OR" # Medida de efeito (OR = Odds Ratio, pode ser "RR" ou "RD" se preferir)
)

# Check network connections
net_con <- netconnection(pw_ash)
net_con

Number of studies: k = 113
Number of pairwise comparisons: m = 125
Number of treatments: n = 22
Number of designs: d = 43
Number of networks: 1

There are three sub-networks:

Network:
- 92 studies
- 100 comparisons
- 22 treatments

Tip

The network is fully connected.

2.0.2 Network

Select the procedures performed

Important

The Network contains 92 studies, 100 comparisons and 22 treatments. This is the main analysis network for binary outcomes.

Code

# Filter data for subnet 1
sr1_ash <- tibble(
    treat1 = net_con$treat1,
    treat2 = net_con$treat2,
    subnet = net_con$subnet
) |>
    filter(subnet == 1)

# Display first rows of comparison table
sr1_ash |> head()

Code

# Unique treatments in subnet 1
unique_treat1_ash <- sr1_ash$treat1 |> unique()
unique_treat2_ash <- sr1_ash$treat2 |> unique()

# Combination of all unique treatments
trat_sr_ash <- unique(c(unique_treat1_ash, unique_treat2_ash))
print(trat_sr_aim)

[1] "Acu"   "rTMS"  "tDCS"  "McT"   "Elec"  "Bal"   "PlaSh" "WlNi"

Code

# Total number of treatments
length(trat_sr_ash)

[1] 22

Code

# Filter pairwise object only for comparisons within network
pw_sr_ash <- pw_ash %>%
    filter(treat1 %in% trat_sr_ash & treat2 %in% trat_sr_ash)

# Number of comparisons in network
nrow(pw_sr_ash)

[1] 161

Code

# Number of unique studies
length(unique(pw_sr_ash$studlab))

[1] 145

Code

# Fit NMA model for subnet 1
nma_sr_ash <- netmeta(
    TE, seTE, treat1, treat2, studlab,
    data = pw_sr_ash,
    n1 = n1,
    n2 = n2,
    random = TRUE,
    common = FALSE,
    ref = "WlNi" # Reference treatment (change if you want another control)
)

Comparisons not considered in network meta-analysis:
                 studlab treat1 treat2 TE seTE
             Bressan2008   AeET   FlET NA   NA
            Brietzke2019   tDCS  PlaSh NA   NA
      Castro-Sanchez2019   DryN   MasT NA   NA
      Castro-Sanchez2020   DryN   Elec NA   NA
             Colbert1999    MfT  PlaSh NA   NA
             daSilva2008   AqET   WlNi NA   NA
               Evcik2002    Bal   WlNi NA   NA
             Fonseca2019   AqET    CBT NA   NA
              Garcia2006    CBT   WlNi NA   NA
     Gomez-Hernandez2019   AeET   MiET NA   NA
              Gowans2001   AqET   WlNi NA   NA
              Harris2005    Acu  PlaSh NA   NA
               Harte2013    Acu  PlaSh NA   NA
                Kayo2012   AeET   ReET NA   NA
                Kayo2012   AeET   WlNi NA   NA
                Kayo2012   ReET   WlNi NA   NA
                King2002    CBT    McT NA   NA
              Mhalla2011   rTMS  PlaSh NA   NA
                 Oka2019    MfT  PlaSh NA   NA
           Schachter2003   AeET   WlNi NA   NA
              Sencan2004   AeET  PlaSh NA   NA
             Sevimli2015   AeET   AqET NA   NA
             Sevimli2015   AeET   MiET NA   NA
             Sevimli2015   AqET   MiET NA   NA
                  To2017   tDCS  PlaSh NA   NA
              Torres2015    MnT    CBT NA   NA
              Ugurlu2017    Acu  PlaSh NA   NA
               Valim2003   AeET   FlET NA   NA
             Vallejo2015    CBT   WlNi NA   NA
 Izquierdo-Alventosa2020   MiET   WlNi NA   NA
        Mingorance2021.2    WBV   WlNi NA   NA
               Baelz2022    Acu  PlaSh NA   NA
               Caumo2023   tDCS  PlaSh NA   NA
              Agdere2025   AeET   WlNi NA   NA
  RodriguezDominguez2026   ReET   MiET NA   NA
               Silva2025   rTMS  PlaSh NA   NA

Code

# Basic model summary
print(nma_sr_ash)

Number of studies: k = 113
Number of pairwise comparisons: m = 125
Number of observations: o = 8216
Number of treatments: n = 22
Number of designs: d = 43

Random effects model

Treatment estimate (other treatments vs 'WlNi'):
          OR            95% CI     z p-value
Acu   0.8385 [0.3347;  2.1008] -0.38  0.7070
AeET  1.3108 [0.6712;  2.5597]  0.79  0.4281
AqET  1.6253 [0.7061;  3.7414]  1.14  0.2535
Bal   1.0503 [0.3263;  3.3803]  0.08  0.9345
CBT   1.2285 [0.9615;  1.5695]  1.65  0.0997
Cry   2.3731 [0.0929; 60.6503]  0.52  0.6012
DryN  1.0000 [0.3828;  2.6122] -0.00  1.0000
Elec  1.0825 [0.5108;  2.2938]  0.21  0.8362
FlET  1.4634 [0.6691;  3.2004]  0.95  0.3403
HtT   0.1765 [0.0180;  1.7256] -1.49  0.1360
MasT  0.7824 [0.3816;  1.6042] -0.67  0.5030
McT   0.9345 [0.6678;  1.3077] -0.40  0.6928
MfT   0.7156 [0.2537;  2.0184] -0.63  0.5270
MiET  0.9231 [0.6166;  1.3820] -0.39  0.6976
MnT   0.5789 [0.2110;  1.5881] -1.06  0.2884
PlaSh 0.8619 [0.4809;  1.5448] -0.50  0.6176
Plt   3.0830 [0.8513; 11.1645]  1.71  0.0864
ReET  1.2542 [0.6563;  2.3968]  0.69  0.4931
rTMS  0.6798 [0.2716;  1.7013] -0.82  0.4096
tDCS  0.7108 [0.2706;  1.8668] -0.69  0.4884
WBV   1.1058 [0.2693;  4.5417]  0.14  0.8890
WlNi       .                 .     .       .

Quantifying heterogeneity / inconsistency:
tau^2 = 0; tau = 0; I^2 = 0% [0.0%; 24.5%]

Tests of heterogeneity (within designs) and inconsistency (between designs):
                    Q d.f. p-value
Total           81.01   98  0.8932
Within designs  61.67   70  0.7510
Between designs 19.34   28  0.8872

Details of network meta-analysis methods:
- Frequentist graph-theoretical approach
- DerSimonian-Laird estimator for tau^2
- Calculation of I^2 based on Q

Note

These three studies were excluded from the model because they reported a standard deviation of zero, which makes division by zero impossible.
- Bressan2008: AeET vs FlET
- Brietzke2019: tDCSvs PlaSh
- Castro-Sanchez2019: DryN vs MasT
- Castro-Sanchez2020: DryN vs Elec
- Colbert1999: MfT vs PlaSh
- daSilva2008: AqET vs WlNi
- Evcik2002: Bal vs WlNi
- Fonseca2019: AqET vs CBT
- Garcia2006: CBT vs WlNi
- Gomez-Hernandez2019: AeET vs MiET
- Gowans2001: AqET vs WlNi
- Harris2005: Acuvs PlaSh
- Harte2013: Acu vs PlaSh
- Kayo2012: AeET vs ReET
- Kayo2012: AeET vs WlNi
- Kayo2012: ReET vs WlNi
- King2002: CBT vs McT
- Mhalla2011: rTMSvs PlaSh
- Oka2019: MfT vs PlaSh
- Schachter2003: AeET vs WlNi
- Sencan2004: AeETvs PlaSh
- Sevimli2015: AeET vs AqET
- Sevimli2015: AeET vs MiET
- Sevimli2015: AqET vs MiET
- To2017: tDCSvs PlaSh
- Torres2015: MnT vs CBT
- Ugurlu2017: Acuvs PlaSh
- Valim2003: AeET vs FlET
- Vallejo2015: CBT vs WlNi
- Izquierdo-Alventosa2020: MiET vs WlNi
- Mingorance2021.2: WBV vs WlNi

Code

# Network plot
netgraph(
    nma_sr_ash,
    seq = "optimal",
    number.of.studies = TRUE,
    cex.points = n.trts,
    labels = paste0(trts, "\n(n=", round(n.trts), ")")
)

Network plot of treatments for Subnet 1 (A | Sh)

Code

# Detailed model summary
summary(nma_sr_ash)

Original data (with adjusted standard errors for multi-arm studies):

                        treat1 treat2      TE   seTE seTE.adj narms multiarm
Albers2018                 MnT   WlNi  0.2032 1.6625   1.6625     2         
Alentorn-Geli2008          McT    WBV -1.1820 1.6833   2.4493     3        *
Alentorn-Geli2008          McT   WlNi -1.7838 1.6048   1.9855     3        *
Alentorn-Geli2008          WBV   WlNi -0.6018 1.1175   1.1951     3        *
Alfano2001                 MfT  PlaSh -0.0174 0.5669   0.5669     2         
Altan2004                 AqET    Bal -1.1856 1.1918   1.1918     2         
Ang2010                    CBT   WlNi -0.1431 1.0694   1.0694     2         
Ardic2007                  Bal   WlNi -2.2203 1.5719   1.5719     2         
Assefi2005                 Acu  PlaSh -0.3533 0.6141   0.6141     2         
Assis2006                 AeET   AqET  1.1701 1.1853   1.1853     2         
Assumpçao2018             FlET   ReET  0.4212 0.8469   0.9695     3        *
Assumpçao2018             FlET   WlNi  0.6931 0.9449   1.1648     3        *
Assumpçao2018             ReET   WlNi  0.2719 0.9835   1.2927     3        *
Astin2003                  CBT    McT -0.0625 0.3536   0.3536     2         
Baumueller2017             CBT   WlNi  0.0000 1.4510   1.4510     2         
Bircan2008                AeET   ReET  0.0000 1.0742   1.0742     2         
Bongi2012                  CBT    McT  0.0000 0.7958   0.7958     2         
Bongi2010                  CBT   WlNi -2.0890 1.5432   1.5432     2         
Bourgault2015              McT   WlNi  1.1331 1.6540   1.6540     2         
Boyer2014                PlaSh   rTMS  0.9008 0.7996   0.7996     2         
Calandre2009              AqET   FlET  0.7538 0.6006   0.6006     2         
Carretero2009            PlaSh   rTMS  0.1671 1.4724   1.4724     2         
Carson2010                 McT   WlNi  0.5725 0.9577   0.9577     2         
Casanueva2014             DryN   WlNi  0.0000 0.4899   0.4899     2         
Collado-Mateo2017         MiET   WlNi -1.9500 1.1044   1.1044     2         
Da Costa2005              MiET   WlNi  0.2693 0.6517   0.6517     2         
Dailey2019                Elec  PlaSh  0.4692 0.2901   0.2901     2         
deMedeiros2020            AqET    Plt -0.4595 0.9703   0.9703     2         
Ekici2017                 MasT    Plt -2.1282 1.1318   1.1318     2         
Ekici2008                 MasT    Plt -0.5754 1.2565   1.2565     2         
Espi-Lopes2016            MiET   WlNi  1.2685 1.1426   1.1426     2         
Fernandes2016             AeET   AqET  0.0846 1.0278   1.0278     2         
Fitzgibbon2018           PlaSh   rTMS -0.6061 1.2939   1.2939     2         
Friedberg2019              CBT   WlNi  0.9555 0.9628   0.9628     2         
Garcia-Martinez2012       MiET   WlNi  0.7732 1.2885   1.2885     2         
Giannotti2014              McT   WlNi -1.6094 1.1673   1.1673     2         
Glasgow2017               ReET   WlNi  1.0217 1.6795   1.6795     2         
Goldway2019                CBT  PlaSh -0.3285 0.8069   0.8069     2         
Gowans1999                 McT   WlNi  1.1474 1.1962   1.1962     2         
Gunther1994                Bal    CBT  1.1756 1.6795   1.6795     2         
Hargrove2012             PlaSh   tDCS  0.3137 0.5862   0.5862     2         
Hsu2010                    CBT   WlNi  1.9459 1.5423   1.5423     2         
Jensen2012                 CBT   WlNi  0.4568 0.7870   0.7870     2         
Jones2002                  McT   ReET  0.0000 0.6362   0.6362     2         
Jones2012                  CBT    McT  2.0268 1.5252   1.5252     2         
Karatay2018                Acu  PlaSh  0.0000 1.2500   1.2500     2         
Kurt2016                   Bal   MiET -0.3151 0.7988   0.7988     2         
Lami2018                   CBT   WlNi  1.1517 0.4971   0.4971     2         
Lauche2016                MasT  PlaSh  0.1788 0.5132   0.5132     2         
Lopes-Rodrigues2012       AqET   FlET  0.1158 0.4815   0.4815     2         
Lopes-Rodrigues2013       AqET   FlET  0.1517 0.5513   0.5513     2         
Luciano2014                CBT   WlNi  0.5942 0.7583   0.7583     2         
Lynch2012                  McT   WlNi  1.5266 0.8100   0.8100     2         
Maestu2013               PlaSh   rTMS -0.1823 0.6619   0.6619     2         
McCrae2019                 CBT   WlNi  0.0364 0.4658   0.4658     2         
Menzies2014                CBT   WlNi  0.7885 0.7508   0.7508     2         
Mist2018                   Acu    CBT -1.0296 0.9599   0.9599     2         
Norrengaard1997           AeET    HtT  2.6391 1.2012   1.5648     3        *
Norrengaard1997           AeET   MiET  1.7047 0.8006   0.8546     3        *
Norrengaard1997            HtT   MiET -0.9343 1.2181   1.6681     3        *
Olivares2011               WBV   WlNi  0.4055 0.9718   0.9718     2         
Paolucci2016               MfT  PlaSh -0.6931 1.2780   1.2780     2         
Paolucci2015              MiET   WlNi  0.4055 0.9789   0.9789     2         
Parra-Delgado2013          CBT   WlNi  1.6720 1.5891   1.5891     2         
Pereira-Pernambuco2018     McT   WlNi -0.1967 0.6187   0.6187     2         
Perez-Aranda2019           CBT   WlNi -0.2933 0.3285   0.3285     2         
Picard2013                 CBT   WlNi -0.7270 1.2521   1.2521     2         
Redondo2004                CBT   MiET  0.1586 0.7610   0.7610     2         
Richards2002              AeET    McT -0.0357 0.4499   0.4499     2         
Rivera2018                 Cry   WlNi  0.8642 1.6536   1.6536     2         
Salaffi2015                McT   WlNi  0.0000 1.0274   1.0274     2         
Schmidt2011                CBT   WlNi  0.5320 0.4669   0.4669     2         
Silva2019                  CBT   ReET -0.1967 0.6282   0.6282     2         
Simister2018               CBT   WlNi  0.8313 0.7545   0.7545     2         
Soares2002                 CBT   WlNi -0.4626 0.8185   0.8185     2         
Sutbeyaz2009               MfT  PlaSh -0.3285 0.8155   0.8155     2         
Tomas-Carus2007b&c        AqET   WlNi  1.0986 1.6676   1.6676     2         
Vas2016                    Acu  PlaSh  1.4240 1.1292   1.1292     2         
Verkaik2013                CBT   WlNi -0.4349 0.6286   0.6286     2         
Wang2018                   McT   MiET -0.3430 0.3500   0.3500     2         
Wicksell2013               CBT   WlNi  0.8755 1.2024   1.2024     2         
Wong2018                   McT   WlNi -1.8036 1.1534   1.1534     2         
Arakaki2021               FlET   ReET  0.7673 0.9078   0.9078     2         
Atan2020                  MiET   WlNi -1.2098 0.9584   0.9584     2         
Barranengoa-Cuadra 2021    CBT   WlNi -0.4203 0.9290   0.9290     2         
Cao2020                    Acu   MasT  0.4418 0.9519   0.9519     2         
Ceballos-Laita2020         McT   MiET -1.2417 1.2153   1.2153     2         
Coste2021                  MnT  PlaSh -0.5960 0.4839   0.4839     2         
Jamison2021               Elec  PlaSh -0.4513 0.4853   0.4853     2         
Nadal-Nicolas2020         MasT  PlaSh -1.9459 1.1711   1.1711     2         
Rodriguez-Mansilla2021     McT   MiET -0.0000 0.4511   0.5643     3        *
Rodriguez-Mansilla2021    MiET   WlNi -0.3805 0.4379   0.5309     3        *
Rodriguez-Mansilla2021     McT   WlNi -0.3805 0.4379   0.5309     3        *
Sarmento2020               McT  PlaSh  0.0000 0.8367   0.8367     2         
Tanwar2020               PlaSh   rTMS  2.2892 1.5061   1.5061     2         
Udina-Cortés2020          Elec  PlaSh  0.2429 0.9706   0.9706     2         
Lacroix2022              PlaSh   rTMS  0.1082 1.0267   1.0267     2         
Paolucci2022               CBT   MiET  0.6931 1.2860   1.2860     2         
Park2021                  FlET   ReET -0.0000 0.7303   0.7303     2         
Samartin-Veiga2022       PlaSh   tDCS -0.0238 0.5576   0.5576     2         
Alptug2023                 MnT   WlNi  1.0460 1.6649   1.6649     2         
Franco2023                AeET    Plt -1.1399 1.6456   1.6456     2         
Lee2024                    CBT    McT  0.6614 1.0234   1.0234     2         
Schulze2023               FlET   MasT  0.5942 1.1107   1.1868     3        *
Schulze2023               MasT   WlNi  1.0986 1.6813   2.4528     3        *
Schulze2023               FlET   WlNi  1.6928 1.6022   1.9808     3        *
Anderson2025               CBT  PlaSh -0.8602 0.9580   0.9580     2         
Badr2024                 PlaSh   rTMS -0.0000 1.4491   1.4491     2         
Betancur2025             PlaSh   tDCS  1.1421 1.6594   1.6594     2         
Carta2024                  CBT   WlNi -0.4274 0.5366   0.5366     2         
Catella2024                CBT   WlNi  0.3781 1.2506   1.2506     2         
Erol2025                   CBT   WlNi  2.0317 1.5307   1.5307     2         
Fernandes2024             FlET   ReET  1.1499 1.1767   1.1767     2         
Ferres2025                 McT   WlNi  0.0000 0.5855   0.5855     2         
Gendreau2024               CBT  PlaSh  1.4663 0.5158   0.5158     2         
Karakoyun2025              CBT   WlNi  0.4055 0.9399   0.9399     2         
Nhu2024                    CBT   WlNi  0.4626 0.9735   0.9735     2         
Ong2024                    CBT   WlNi  0.4055 0.5725   0.5725     2         
PontesSilva2026           AeET   ReET -0.4616 0.6030   0.6030     2         
Sari2025                   McT   WlNi -2.1340 1.5530   1.5530     2         
Taub2024                   CBT   WlNi  0.2630 0.5840   0.5840     2         
Tiwari2024               PlaSh   rTMS -0.0000 1.0572   1.0572     2         
Ughreja2024               MasT  PlaSh -0.1919 0.5321   0.6013     3        *
Ughreja2024               MasT   MiET  0.2187 0.5810   0.6855     3        *
Ughreja2024               MiET  PlaSh -0.4106 0.6458   0.9147     3        *

Number of treatment arms per study (by decreasing number of arms):
                        narms multiarm
Alentorn-Geli2008           3        *
Assumpçao2018               3        *
Norrengaard1997             3        *
Rodriguez-Mansilla2021      3        *
Schulze2023                 3        *
Ughreja2024                 3        *
Albers2018                  2         
Alfano2001                  2         
Altan2004                   2         
Ang2010                     2         
Ardic2007                   2         
Assefi2005                  2         
Assis2006                   2         
Astin2003                   2         
Baumueller2017              2         
Bircan2008                  2         
Bongi2012                   2         
Bongi2010                   2         
Bourgault2015               2         
Boyer2014                   2         
Calandre2009                2         
Carretero2009               2         
Carson2010                  2         
Casanueva2014               2         
Collado-Mateo2017           2         
Da Costa2005                2         
Dailey2019                  2         
deMedeiros2020              2         
Ekici2017                   2         
Ekici2008                   2         
Espi-Lopes2016              2         
Fernandes2016               2         
Fitzgibbon2018              2         
Friedberg2019               2         
Garcia-Martinez2012         2         
Giannotti2014               2         
Glasgow2017                 2         
Goldway2019                 2         
Gowans1999                  2         
Gunther1994                 2         
Hargrove2012                2         
Hsu2010                     2         
Jensen2012                  2         
Jones2002                   2         
Jones2012                   2         
Karatay2018                 2         
Kurt2016                    2         
Lami2018                    2         
Lauche2016                  2         
Lopes-Rodrigues2012         2         
Lopes-Rodrigues2013         2         
Luciano2014                 2         
Lynch2012                   2         
Maestu2013                  2         
McCrae2019                  2         
Menzies2014                 2         
Mist2018                    2         
Olivares2011                2         
Paolucci2016                2         
Paolucci2015                2         
Parra-Delgado2013           2         
Pereira-Pernambuco2018      2         
Perez-Aranda2019            2         
Picard2013                  2         
Redondo2004                 2         
Richards2002                2         
Rivera2018                  2         
Salaffi2015                 2         
Schmidt2011                 2         
Silva2019                   2         
Simister2018                2         
Soares2002                  2         
Sutbeyaz2009                2         
Tomas-Carus2007b&c          2         
Vas2016                     2         
Verkaik2013                 2         
Wang2018                    2         
Wicksell2013                2         
Wong2018                    2         
Arakaki2021                 2         
Atan2020                    2         
Barranengoa-Cuadra 2021     2         
Cao2020                     2         
Ceballos-Laita2020          2         
Coste2021                   2         
Jamison2021                 2         
Nadal-Nicolas2020           2         
Sarmento2020                2         
Tanwar2020                  2         
Udina-Cortés2020            2         
Lacroix2022                 2         
Paolucci2022                2         
Park2021                    2         
Samartin-Veiga2022          2         
Alptug2023                  2         
Franco2023                  2         
Lee2024                     2         
Anderson2025                2         
Badr2024                    2         
Betancur2025                2         
Carta2024                   2         
Catella2024                 2         
Erol2025                    2         
Fernandes2024               2         
Ferres2025                  2         
Gendreau2024                2         
Karakoyun2025               2         
Nhu2024                     2         
Ong2024                     2         
PontesSilva2026             2         
Sari2025                    2         
Taub2024                    2         
Tiwari2024                  2         

Results (random effects model):

                        treat1 treat2     OR            95% CI
Albers2018                 MnT   WlNi 0.5789 [0.2110;  1.5881]
Alentorn-Geli2008          McT    WBV 0.8451 [0.1992;  3.5860]
Alentorn-Geli2008          McT   WlNi 0.9345 [0.6678;  1.3077]
Alentorn-Geli2008          WBV   WlNi 1.1058 [0.2693;  4.5417]
Alfano2001                 MfT  PlaSh 0.8303 [0.3523;  1.9565]
Altan2004                 AqET    Bal 1.5475 [0.4239;  5.6498]
Ang2010                    CBT   WlNi 1.2285 [0.9615;  1.5695]
Ardic2007                  Bal   WlNi 1.0503 [0.3263;  3.3803]
Assefi2005                 Acu  PlaSh 0.9729 [0.4374;  2.1641]
Assis2006                 AeET   AqET 0.8064 [0.3421;  1.9013]
Assumpçao2018             FlET   ReET 1.1668 [0.5753;  2.3663]
Assumpçao2018             FlET   WlNi 1.4634 [0.6691;  3.2004]
Assumpçao2018             ReET   WlNi 1.2542 [0.6563;  2.3968]
Astin2003                  CBT    McT 1.3145 [0.9241;  1.8699]
Baumueller2017             CBT   WlNi 1.2285 [0.9615;  1.5695]
Bircan2008                AeET   ReET 1.0451 [0.5216;  2.0940]
Bongi2012                  CBT    McT 1.3145 [0.9241;  1.8699]
Bongi2010                  CBT   WlNi 1.2285 [0.9615;  1.5695]
Bourgault2015              McT   WlNi 0.9345 [0.6678;  1.3077]
Boyer2014                PlaSh   rTMS 1.2679 [0.6247;  2.5735]
Calandre2009              AqET   FlET 1.1107 [0.6349;  1.9429]
Carretero2009            PlaSh   rTMS 1.2679 [0.6247;  2.5735]
Carson2010                 McT   WlNi 0.9345 [0.6678;  1.3077]
Casanueva2014             DryN   WlNi 1.0000 [0.3828;  2.6122]
Collado-Mateo2017         MiET   WlNi 0.9231 [0.6166;  1.3820]
Da Costa2005              MiET   WlNi 0.9231 [0.6166;  1.3820]
Dailey2019                Elec  PlaSh 1.2559 [0.7828;  2.0149]
deMedeiros2020            AqET    Plt 0.5272 [0.1454;  1.9116]
Ekici2017                 MasT    Plt 0.2538 [0.0731;  0.8812]
Ekici2008                 MasT    Plt 0.2538 [0.0731;  0.8812]
Espi-Lopes2016            MiET   WlNi 0.9231 [0.6166;  1.3820]
Fernandes2016             AeET   AqET 0.8064 [0.3421;  1.9013]
Fitzgibbon2018           PlaSh   rTMS 1.2679 [0.6247;  2.5735]
Friedberg2019              CBT   WlNi 1.2285 [0.9615;  1.5695]
Garcia-Martinez2012       MiET   WlNi 0.9231 [0.6166;  1.3820]
Giannotti2014              McT   WlNi 0.9345 [0.6678;  1.3077]
Glasgow2017               ReET   WlNi 1.2542 [0.6563;  2.3968]
Goldway2019                CBT  PlaSh 1.4253 [0.8122;  2.5012]
Gowans1999                 McT   WlNi 0.9345 [0.6678;  1.3077]
Gunther1994                Bal    CBT 0.8549 [0.2642;  2.7671]
Hargrove2012             PlaSh   tDCS 1.2126 [0.5618;  2.6172]
Hsu2010                    CBT   WlNi 1.2285 [0.9615;  1.5695]
Jensen2012                 CBT   WlNi 1.2285 [0.9615;  1.5695]
Jones2002                  McT   ReET 0.7451 [0.3930;  1.4126]
Jones2012                  CBT    McT 1.3145 [0.9241;  1.8699]
Karatay2018                Acu  PlaSh 0.9729 [0.4374;  2.1641]
Kurt2016                   Bal   MiET 1.1378 [0.3589;  3.6065]
Lami2018                   CBT   WlNi 1.2285 [0.9615;  1.5695]
Lauche2016                MasT  PlaSh 0.9078 [0.4976;  1.6561]
Lopes-Rodrigues2012       AqET   FlET 1.1107 [0.6349;  1.9429]
Lopes-Rodrigues2013       AqET   FlET 1.1107 [0.6349;  1.9429]
Luciano2014                CBT   WlNi 1.2285 [0.9615;  1.5695]
Lynch2012                  McT   WlNi 0.9345 [0.6678;  1.3077]
Maestu2013               PlaSh   rTMS 1.2679 [0.6247;  2.5735]
McCrae2019                 CBT   WlNi 1.2285 [0.9615;  1.5695]
Menzies2014                CBT   WlNi 1.2285 [0.9615;  1.5695]
Mist2018                   Acu    CBT 0.6826 [0.2767;  1.6837]
Norrengaard1997           AeET    HtT 7.4276 [0.7751; 71.1803]
Norrengaard1997           AeET   MiET 1.4199 [0.7120;  2.8317]
Norrengaard1997            HtT   MiET 0.1912 [0.0198;  1.8444]
Olivares2011               WBV   WlNi 1.1058 [0.2693;  4.5417]
Paolucci2016               MfT  PlaSh 0.8303 [0.3523;  1.9565]
Paolucci2015              MiET   WlNi 0.9231 [0.6166;  1.3820]
Parra-Delgado2013          CBT   WlNi 1.2285 [0.9615;  1.5695]
Pereira-Pernambuco2018     McT   WlNi 0.9345 [0.6678;  1.3077]
Perez-Aranda2019           CBT   WlNi 1.2285 [0.9615;  1.5695]
Picard2013                 CBT   WlNi 1.2285 [0.9615;  1.5695]
Redondo2004                CBT   MiET 1.3308 [0.8688;  2.0385]
Richards2002              AeET    McT 1.4026 [0.7454;  2.6393]
Rivera2018                 Cry   WlNi 2.3731 [0.0929; 60.6503]
Salaffi2015                McT   WlNi 0.9345 [0.6678;  1.3077]
Schmidt2011                CBT   WlNi 1.2285 [0.9615;  1.5695]
Silva2019                  CBT   ReET 0.9795 [0.5123;  1.8727]
Simister2018               CBT   WlNi 1.2285 [0.9615;  1.5695]
Soares2002                 CBT   WlNi 1.2285 [0.9615;  1.5695]
Sutbeyaz2009               MfT  PlaSh 0.8303 [0.3523;  1.9565]
Tomas-Carus2007b&c        AqET   WlNi 1.6253 [0.7061;  3.7414]
Vas2016                    Acu  PlaSh 0.9729 [0.4374;  2.1641]
Verkaik2013                CBT   WlNi 1.2285 [0.9615;  1.5695]
Wang2018                   McT   MiET 1.0124 [0.6763;  1.5154]
Wicksell2013               CBT   WlNi 1.2285 [0.9615;  1.5695]
Wong2018                   McT   WlNi 0.9345 [0.6678;  1.3077]
Arakaki2021               FlET   ReET 1.1668 [0.5753;  2.3663]
Atan2020                  MiET   WlNi 0.9231 [0.6166;  1.3820]
Barranengoa-Cuadra 2021    CBT   WlNi 1.2285 [0.9615;  1.5695]
Cao2020                    Acu   MasT 1.0717 [0.4279;  2.6845]
Ceballos-Laita2020         McT   MiET 1.0124 [0.6763;  1.5154]
Coste2021                  MnT  PlaSh 0.6717 [0.2783;  1.6212]
Jamison2021               Elec  PlaSh 1.2559 [0.7828;  2.0149]
Nadal-Nicolas2020         MasT  PlaSh 0.9078 [0.4976;  1.6561]
Rodriguez-Mansilla2021     McT   MiET 1.0124 [0.6763;  1.5154]
Rodriguez-Mansilla2021    MiET   WlNi 0.9231 [0.6166;  1.3820]
Rodriguez-Mansilla2021     McT   WlNi 0.9345 [0.6678;  1.3077]
Sarmento2020               McT  PlaSh 1.0843 [0.5901;  1.9922]
Tanwar2020               PlaSh   rTMS 1.2679 [0.6247;  2.5735]
Udina-Cortés2020          Elec  PlaSh 1.2559 [0.7828;  2.0149]
Lacroix2022              PlaSh   rTMS 1.2679 [0.6247;  2.5735]
Paolucci2022               CBT   MiET 1.3308 [0.8688;  2.0385]
Park2021                  FlET   ReET 1.1668 [0.5753;  2.3663]
Samartin-Veiga2022       PlaSh   tDCS 1.2126 [0.5618;  2.6172]
Alptug2023                 MnT   WlNi 0.5789 [0.2110;  1.5881]
Franco2023                AeET    Plt 0.4252 [0.1128;  1.6027]
Lee2024                    CBT    McT 1.3145 [0.9241;  1.8699]
Schulze2023               FlET   MasT 1.8703 [0.7261;  4.8181]
Schulze2023               MasT   WlNi 0.7824 [0.3816;  1.6042]
Schulze2023               FlET   WlNi 1.4634 [0.6691;  3.2004]
Anderson2025               CBT  PlaSh 1.4253 [0.8122;  2.5012]
Badr2024                 PlaSh   rTMS 1.2679 [0.6247;  2.5735]
Betancur2025             PlaSh   tDCS 1.2126 [0.5618;  2.6172]
Carta2024                  CBT   WlNi 1.2285 [0.9615;  1.5695]
Catella2024                CBT   WlNi 1.2285 [0.9615;  1.5695]
Erol2025                   CBT   WlNi 1.2285 [0.9615;  1.5695]
Fernandes2024             FlET   ReET 1.1668 [0.5753;  2.3663]
Ferres2025                 McT   WlNi 0.9345 [0.6678;  1.3077]
Gendreau2024               CBT  PlaSh 1.4253 [0.8122;  2.5012]
Karakoyun2025              CBT   WlNi 1.2285 [0.9615;  1.5695]
Nhu2024                    CBT   WlNi 1.2285 [0.9615;  1.5695]
Ong2024                    CBT   WlNi 1.2285 [0.9615;  1.5695]
PontesSilva2026           AeET   ReET 1.0451 [0.5216;  2.0940]
Sari2025                   McT   WlNi 0.9345 [0.6678;  1.3077]
Taub2024                   CBT   WlNi 1.2285 [0.9615;  1.5695]
Tiwari2024               PlaSh   rTMS 1.2679 [0.6247;  2.5735]
Ughreja2024               MasT  PlaSh 0.9078 [0.4976;  1.6561]
Ughreja2024               MasT   MiET 0.8476 [0.4083;  1.7595]
Ughreja2024               MiET  PlaSh 1.0710 [0.5717;  2.0066]

Number of studies: k = 113
Number of pairwise comparisons: m = 125
Number of observations: o = 8216
Number of treatments: n = 22
Number of designs: d = 43

Random effects model

Treatment estimate (other treatments vs 'WlNi'):
          OR            95% CI     z p-value
Acu   0.8385 [0.3347;  2.1008] -0.38  0.7070
AeET  1.3108 [0.6712;  2.5597]  0.79  0.4281
AqET  1.6253 [0.7061;  3.7414]  1.14  0.2535
Bal   1.0503 [0.3263;  3.3803]  0.08  0.9345
CBT   1.2285 [0.9615;  1.5695]  1.65  0.0997
Cry   2.3731 [0.0929; 60.6503]  0.52  0.6012
DryN  1.0000 [0.3828;  2.6122] -0.00  1.0000
Elec  1.0825 [0.5108;  2.2938]  0.21  0.8362
FlET  1.4634 [0.6691;  3.2004]  0.95  0.3403
HtT   0.1765 [0.0180;  1.7256] -1.49  0.1360
MasT  0.7824 [0.3816;  1.6042] -0.67  0.5030
McT   0.9345 [0.6678;  1.3077] -0.40  0.6928
MfT   0.7156 [0.2537;  2.0184] -0.63  0.5270
MiET  0.9231 [0.6166;  1.3820] -0.39  0.6976
MnT   0.5789 [0.2110;  1.5881] -1.06  0.2884
PlaSh 0.8619 [0.4809;  1.5448] -0.50  0.6176
Plt   3.0830 [0.8513; 11.1645]  1.71  0.0864
ReET  1.2542 [0.6563;  2.3968]  0.69  0.4931
rTMS  0.6798 [0.2716;  1.7013] -0.82  0.4096
tDCS  0.7108 [0.2706;  1.8668] -0.69  0.4884
WBV   1.1058 [0.2693;  4.5417]  0.14  0.8890
WlNi       .                 .     .       .

Quantifying heterogeneity / inconsistency:
tau^2 = 0; tau = 0; I^2 = 0% [0.0%; 24.5%]

Tests of heterogeneity (within designs) and inconsistency (between designs):
                    Q d.f. p-value
Total           81.01   98  0.8932
Within designs  61.67   70  0.7510
Between designs 19.34   28  0.8872

Details of network meta-analysis methods:
- Frequentist graph-theoretical approach
- DerSimonian-Laird estimator for tau^2
- Calculation of I^2 based on Q

Code

# Forest plot
forest(nma_sr_ash)

Forest plot of treatment effects for Subnet 1 (A | Sh)

Code

# Pairwise comparison table with confidence intervals
render_league_table(nma_sr_ash)

Table 25: League table (random effects model) for Subnet 1 (A | Sh)

Comparison	Acu	AeET	AqET	Bal	CBT	Cry	DryN	Elec	FlET	HtT	MasT	McT	MfT	MiET	MnT	PlaSh	Plt	ReET	rTMS	tDCS	WBV	WlNi
Acu	Acu	.	.	.	0.3571 [0.0544; 2.3438]	.	.	.	.	.	1.5556 [0.2408; 10.0492]	.	.	.	.	1.0451 [0.3959; 2.7591]	.	.	.	.	.	.
AeET	0.6397 [0.2152; 1.9013]	AeET	1.7341 [0.3785; 7.9442]	.	.	.	.	.	.	14.0000 [1.3295; 147.4289]	.	0.9649 [0.3995; 2.3304]	.	5.5000 [1.1453; 26.4124]	.	.	0.3199 [0.0127; 8.0479]	0.7040 [0.2512; 1.9729]	.	.	.	.
AqET	0.5159 [0.1583; 1.6815]	0.8064 [0.3421; 1.9013]	AqET	0.3056 [0.0296; 3.1592]	.	.	.	.	1.3467 [0.7329; 2.4746]	.	.	.	.	.	.	.	0.6316 [0.0943; 4.2302]	.	.	.	.	3.0000 [0.1142; 78.8136]
Bal	0.7984 [0.1866; 3.4157]	1.2480 [0.3462; 4.4988]	1.5475 [0.4239; 5.6498]	Bal	3.2400 [0.1205; 87.1251]	.	.	.	.	.	.	.	.	0.7297 [0.1525; 3.4925]	.	.	.	.	.	.	.	0.1086 [0.0050; 2.3645]
CBT	0.6826 [0.2767; 1.6837]	1.0670 [0.5429; 2.0969]	1.3231 [0.5714; 3.0635]	0.8549 [0.2642; 2.7671]	CBT	.	.	.	.	.	.	1.0958 [0.6062; 1.9809]	.	1.3461 [0.3729; 4.8594]	.	1.8918 [0.8710; 4.1092]	.	0.8214 [0.2398; 2.8140]	.	.	.	1.2373 [0.9435; 1.6226]
Cry	0.3533 [0.0122; 10.2596]	0.5523 [0.0202; 15.1151]	0.6849 [0.0241; 19.4518]	0.4426 [0.0141; 13.8753]	0.5177 [0.0201; 13.3526]	Cry	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	2.3731 [0.0929; 60.6503]
DryN	0.8385 [0.2221; 3.1664]	1.3108 [0.4066; 4.2251]	1.6253 [0.4557; 5.7971]	1.0503 [0.2314; 4.7673]	1.2285 [0.4560; 3.3092]	2.3731 [0.0808; 69.7118]	DryN	.	.	.	.	.	.	.	.	.	.	.	.	.	.	1.0000 [0.3828; 2.6122]
Elec	0.7746 [0.3060; 1.9609]	1.2109 [0.4677; 3.1353]	1.5015 [0.5214; 4.3239]	0.9703 [0.2507; 3.7550]	1.1349 [0.5444; 2.3659]	2.1923 [0.0787; 61.0537]	0.9238 [0.2730; 3.1260]	Elec	.	.	.	.	.	.	.	1.2559 [0.7828; 2.0149]	.	.	.	.	.	.
FlET	0.5730 [0.1818; 1.8059]	0.8957 [0.3894; 2.0605]	1.1107 [0.6349; 1.9429]	0.7177 [0.1956; 2.6340]	0.8395 [0.3816; 1.8467]	1.6217 [0.0578; 45.4909]	0.6833 [0.1980; 2.3583]	0.7397 [0.2667; 2.0514]	FlET	.	1.8116 [0.2054; 15.9764]	.	.	.	.	.	.	1.5703 [0.6651; 3.7075]	.	.	.	2.5886 [0.5251; 12.7605]
HtT	4.7517 [0.4155; 54.3449]	7.4276 [0.7751; 71.1803]	9.2103 [0.8592; 98.7361]	5.9516 [0.4759; 74.4366]	6.9613 [0.7098; 68.2698]	13.4479 [0.2557; 707.3146]	5.6667 [0.4774; 67.2682]	6.1340 [0.5688; 66.1487]	8.2925 [0.7829; 87.8396]	HtT	.	.	.	0.3929 [0.0361; 4.2764]	.	.	.	.	.	.	.	.
MasT	1.0717 [0.4279; 2.6845]	1.6753 [0.6799; 4.1278]	2.0773 [0.7762; 5.5595]	1.3423 [0.3586; 5.0250]	1.5701 [0.7722; 3.1924]	3.0331 [0.1097; 83.8546]	1.2781 [0.3854; 4.2390]	1.3835 [0.6439; 2.9726]	1.8703 [0.7261; 4.8181]	0.2255 [0.0213; 2.3874]	MasT	.	.	1.2444 [0.3985; 3.8862]	.	0.8387 [0.4205; 1.6729]	0.2387 [0.0459; 1.2405]	.	.	.	.	3.0000 [0.1112; 80.9506]
McT	0.8973 [0.3516; 2.2899]	1.4026 [0.7454; 2.6393]	1.7392 [0.7538; 4.0131]	1.1239 [0.3457; 3.6535]	1.3145 [0.9241; 1.8699]	2.5394 [0.0977; 66.0374]	1.0701 [0.3869; 2.9594]	1.1583 [0.5361; 2.5027]	1.5659 [0.7128; 3.4400]	0.1888 [0.0194; 1.8361]	0.8372 [0.4021; 1.7432]	McT	.	0.7675 [0.4524; 1.3019]	.	1.0000 [0.1940; 5.1543]	.	1.0000 [0.2874; 3.4797]	.	.	0.3067 [0.0113; 8.3086]	0.8707 [0.5461; 1.3881]
MfT	1.1718 [0.3629; 3.7837]	1.8317 [0.5572; 6.0218]	2.2713 [0.6336; 8.1424]	1.4677 [0.3176; 6.7822]	1.7167 [0.6158; 4.7856]	3.3164 [0.1104; 99.6469]	1.3975 [0.3401; 5.7425]	1.5127 [0.5684; 4.0261]	2.0450 [0.5884; 7.1072]	0.2466 [0.0206; 2.9544]	1.0934 [0.3838; 3.1152]	1.3059 [0.4565; 3.7361]	MfT	.	.	0.8303 [0.3523; 1.9565]	.	.	.	.	.	.
MiET	0.9084 [0.3513; 2.3490]	1.4199 [0.7120; 2.8317]	1.7607 [0.7406; 4.1863]	1.1378 [0.3589; 3.6065]	1.3308 [0.8688; 2.0385]	2.5708 [0.0981; 67.3681]	1.0833 [0.3823; 3.0696]	1.1726 [0.5344; 2.5732]	1.5853 [0.6966; 3.6078]	0.1912 [0.0198; 1.8444]	0.8476 [0.4083; 1.7595]	1.0124 [0.6763; 1.5154]	0.7752 [0.2679; 2.2432]	MiET	.	0.6633 [0.1871; 2.3517]	.	.	.	.	.	0.8168 [0.4627; 1.4421]
MnT	1.4484 [0.4444; 4.7206]	2.2641 [0.7009; 7.3138]	2.8075 [0.7946; 9.9205]	1.8142 [0.3981; 8.2668]	2.1220 [0.7782; 5.7863]	4.0993 [0.1376; 122.1433]	1.7274 [0.4290; 6.9556]	1.8698 [0.6879; 5.0823]	2.5278 [0.7384; 8.6538]	0.3048 [0.0257; 3.6215]	1.3515 [0.4711; 3.8775]	1.6142 [0.5766; 4.5196]	1.2361 [0.3616; 4.2259]	1.5945 [0.5619; 4.5248]	MnT	0.5510 [0.2135; 1.4224]	.	.	.	.	.	1.8663 [0.1860; 18.7220]
PlaSh	0.9729 [0.4374; 2.1641]	1.5208 [0.6661; 3.4724]	1.8858 [0.7321; 4.8575]	1.2186 [0.3429; 4.3307]	1.4253 [0.8122; 2.5012]	2.7534 [0.1023; 74.1344]	1.1603 [0.3772; 3.5688]	1.2559 [0.7828; 2.0149]	1.6979 [0.6876; 4.1924]	0.2047 [0.0199; 2.1057]	0.9078 [0.4976; 1.6561]	1.0843 [0.5901; 1.9922]	0.8303 [0.3523; 1.9565]	1.0710 [0.5717; 2.0066]	0.6717 [0.2783; 1.6212]	PlaSh	.	.	1.2679 [0.6247; 2.5735]	1.2126 [0.5618; 2.6172]	.	.
Plt	0.2720 [0.0622; 1.1891]	0.4252 [0.1128; 1.6027]	0.5272 [0.1454; 1.9116]	0.3407 [0.0642; 1.8089]	0.3985 [0.1101; 1.4423]	0.7698 [0.0235; 25.1627]	0.3244 [0.0651; 1.6156]	0.3511 [0.0882; 1.3974]	0.4747 [0.1272; 1.7712]	0.0572 [0.0044; 0.7489]	0.2538 [0.0731; 0.8812]	0.3031 [0.0835; 1.1004]	0.2321 [0.0490; 1.0995]	0.2994 [0.0816; 1.0983]	0.1878 [0.0398; 0.8861]	0.2796 [0.0764; 1.0236]	Plt	.	.	.	.	.
ReET	0.6686 [0.2276; 1.9640]	1.0451 [0.5216; 2.0940]	1.2960 [0.5789; 2.9014]	0.8374 [0.2343; 2.9932]	0.9795 [0.5123; 1.8727]	1.8922 [0.0694; 51.5593]	0.7973 [0.2504; 2.5389]	0.8631 [0.3375; 2.2074]	1.1668 [0.5753; 2.3663]	0.1407 [0.0139; 1.4280]	0.6238 [0.2564; 1.5181]	0.7451 [0.3930; 1.4126]	0.5706 [0.1753; 1.8574]	0.7360 [0.3658; 1.4809]	0.4616 [0.1444; 1.4758]	0.6872 [0.3053; 1.5469]	2.4582 [0.6474; 9.3344]	ReET	.	.	.	1.5894 [0.3012; 8.3878]
rTMS	1.2335 [0.4240; 3.5885]	1.9282 [0.6499; 5.7211]	2.3910 [0.7335; 7.7943]	1.5450 [0.3616; 6.6017]	1.8072 [0.7317; 4.4632]	3.4911 [0.1203; 101.3392]	1.4711 [0.3898; 5.5512]	1.5924 [0.6798; 3.7302]	2.1528 [0.6829; 6.7860]	0.2596 [0.0227; 2.9658]	1.1510 [0.4547; 2.9136]	1.3748 [0.5406; 3.4961]	1.0527 [0.3463; 3.1997]	1.3580 [0.5272; 3.4980]	0.8516 [0.2750; 2.6371]	1.2679 [0.6247; 2.5735]	4.5353 [1.0341; 19.8909]	1.8450 [0.6286; 5.4155]	rTMS	.	.	.
tDCS	1.1797 [0.3890; 3.5780]	1.8441 [0.5966; 5.7002]	2.2867 [0.6755; 7.7414]	1.4776 [0.3353; 6.5119]	1.7283 [0.6664; 4.4822]	3.3388 [0.1135; 98.2295]	1.4069 [0.3605; 5.4912]	1.5229 [0.6173; 3.7569]	2.0588 [0.6282; 6.7471]	0.2483 [0.0213; 2.8895]	1.1008 [0.4146; 2.9224]	1.3148 [0.4931; 3.5059]	1.0068 [0.3182; 3.1853]	1.2987 [0.4811; 3.5057]	0.8145 [0.2529; 2.6236]	1.2126 [0.5618; 2.6172]	4.3374 [0.9594; 19.6099]	1.7645 [0.5768; 5.3979]	0.9564 [0.3362; 2.7207]	tDCS	.	.
WBV	0.7583 [0.1409; 4.0807]	1.1853 [0.2494; 5.6337]	1.4698 [0.2858; 7.5581]	0.9497 [0.1521; 5.9295]	1.1109 [0.2652; 4.6528]	2.1460 [0.0625; 73.6289]	0.9043 [0.1639; 4.9904]	0.9789 [0.1981; 4.8370]	1.3233 [0.2640; 6.6344]	0.1596 [0.0109; 2.3277]	0.7075 [0.1454; 3.4427]	0.8451 [0.1992; 3.5860]	0.6471 [0.1124; 3.7252]	0.8348 [0.1927; 3.6156]	0.5235 [0.0924; 2.9658]	0.7794 [0.1694; 3.5854]	2.7879 [0.4134; 18.7993]	1.1341 [0.2406; 5.3469]	0.6147 [0.1143; 3.3059]	0.6428 [0.1164; 3.5505]	WBV	0.9721 [0.2310; 4.0920]
WlNi	0.8385 [0.3347; 2.1008]	1.3108 [0.6712; 2.5597]	1.6253 [0.7061; 3.7414]	1.0503 [0.3263; 3.3803]	1.2285 [0.9615; 1.5695]	2.3731 [0.0929; 60.6503]	1.0000 [0.3828; 2.6122]	1.0825 [0.5108; 2.2938]	1.4634 [0.6691; 3.2004]	0.1765 [0.0180; 1.7256]	0.7824 [0.3816; 1.6042]	0.9345 [0.6678; 1.3077]	0.7156 [0.2537; 2.0184]	0.9231 [0.6166; 1.3820]	0.5789 [0.2110; 1.5881]	0.8619 [0.4809; 1.5448]	3.0830 [0.8513; 11.1645]	1.2542 [0.6563; 2.3968]	0.6798 [0.2716; 1.7013]	0.7108 [0.2706; 1.8668]	1.1058 [0.2693; 4.5417]	WlNi

Code

# Treatment ranking
ranking_sr_ash <- netrank(nma_sr_ash, small.values = "good")
print(ranking_sr_ash)

      P-score
HtT    0.9165
MnT    0.7811
rTMS   0.7202
tDCS   0.6903
MfT    0.6789
MasT   0.6610
PlaSh  0.6007
Acu    0.5987
MiET   0.5659
McT    0.5611
WlNi   0.5025
DryN   0.4982
Bal    0.4744
WBV    0.4535
Elec   0.4258
ReET   0.3496
CBT    0.3219
AeET   0.3203
Cry    0.3053
FlET   0.2714
AqET   0.2203
Plt    0.0825

Code

# Rankogram
ran_sr_ash <- rankogram(nma_sr_ash)
plot(ran_sr_ash)

Rankogram of treatments for Subnet 1 (A | Sh)

Code

# Design decomposition
decomp_sr_ash <- decomp.design(nma_sr_ash)
print(decomp_sr_ash)

Q statistics to assess homogeneity / consistency

                    Q df p-value
Total           81.01 98  0.8932
Within designs  61.67 70  0.7510
Between designs 19.34 28  0.8872

Design-specific decomposition of within-designs Q statistic

     Design     Q df p-value
  CBT:PlaSh  6.46  2  0.0396
 MasT:PlaSh  2.76  1  0.0966
   WlNi:McT 11.63  9  0.2350
  WlNi:MiET  6.62  5  0.2505
 Elec:PlaSh  2.65  2  0.2657
   MasT:Plt  0.84  1  0.3585
  Acu:PlaSh  1.91  2  0.3842
   McT:MiET  0.50  1  0.4773
  AeET:AqET  0.48  1  0.4890
    CBT:McT  2.12  3  0.5473
  FlET:ReET  0.86  2  0.6521
  AqET:FlET  0.79  2  0.6738
  AeET:ReET  0.14  1  0.7078
   WlNi:MnT  0.13  1  0.7202
   CBT:MiET  0.13  1  0.7206
 PlaSh:tDCS  0.52  2  0.7708
   WlNi:CBT 19.38 25  0.7788
 PlaSh:rTMS  3.47  7  0.8387
  MfT:PlaSh  0.28  2  0.8709

Between-designs Q statistic after detaching of single designs
(influential designs have p-value markedly different from 0.8872)

 Detached design     Q df p-value
        AqET:Bal 16.67 27  0.9390
        McT:MiET 16.80 27  0.9359
       AqET:FlET 16.86 27  0.9345
        WlNi:Bal 16.91 27  0.9334
       AeET:AqET 17.92 27  0.9060
        AeET:McT 17.92 27  0.9060
       MnT:PlaSh 18.12 27  0.8999
        WlNi:MnT 18.12 27  0.8999
       CBT:PlaSh 18.26 27  0.8953
       AeET:ReET 18.31 27  0.8939
         Bal:CBT 18.62 27  0.8834
       FlET:ReET 18.63 27  0.8832
        Bal:MiET 18.67 27  0.8819
         Acu:CBT 18.75 27  0.8789
         CBT:McT 18.78 27  0.8779
    WlNi:McT:WBV 17.97 26  0.8769
        McT:ReET 19.05 27  0.8681
       WlNi:ReET 19.11 27  0.8660
        WlNi:WBV 19.12 27  0.8655
        Acu:MasT 19.14 27  0.8648
       WlNi:AqET 19.20 27  0.8627
        WlNi:McT 19.20 27  0.8626
        CBT:ReET 19.23 27  0.8613
       Acu:PlaSh 19.28 27  0.8597
        AqET:Plt 19.28 27  0.8596
 MasT:MiET:PlaSh 18.45 26  0.8588
        AeET:Plt 19.31 27  0.8586
      MasT:PlaSh 19.31 27  0.8585
        WlNi:CBT 19.33 27  0.8578
        MasT:Plt 19.33 27  0.8577
       McT:PlaSh 19.33 27  0.8576
       WlNi:MiET 19.34 27  0.8573
        CBT:MiET 19.34 27  0.8572
   WlNi:McT:MiET 18.53 26  0.8557
  WlNi:FlET:MasT 18.57 26  0.8539
  WlNi:FlET:ReET 19.15 26  0.8299

Q statistic to assess consistency under the assumption of
a full design-by-treatment interaction random effects model

                    Q df p-value tau.within tau2.within
Between designs 19.34 28  0.8872          0           0

Code

# Node-splitting analysis to detect inconsistency
split_sr_ash <- netsplit(nma_sr_ash) # , show = "all")
render_netsplit_table(split_sr_ash)

Table 26: Node-splitting analysis for Subnet 1 (A | Sh)

comparison	TE	seTE	lower	upper	statistic	p
Acu:AeET	-0.4467	0.5558	-1.5360	0.6426	-0.8038	0.4215
Acu:AqET	-0.6618	0.6028	-1.8433	0.5197	-1.0979	0.2723
Acu:Bal	-0.2252	0.7416	-1.6787	1.2284	-0.3036	0.7614
Acu:CBT	-0.3819	0.4607	-1.2847	0.5210	-0.8290	0.4071
Acu:Cry	-1.0403	1.7187	-4.4088	2.3282	-0.6053	0.5450
Acu:DryN	-0.1761	0.6779	-1.5048	1.1526	-0.2598	0.7950
Acu:Elec	-0.2554	0.4739	-1.1841	0.6734	-0.5389	0.5900
Acu:FlET	-0.5569	0.5857	-1.7048	0.5910	-0.9508	0.3417
Acu:HtT	1.5585	1.2433	-0.8784	3.9954	1.2535	0.2100
Acu:MasT	0.0693	0.4685	-0.8490	0.9875	0.1479	0.8825
Acu:McT	-0.1084	0.4780	-1.0453	0.8285	-0.2267	0.8206
Acu:MfT	0.1585	0.5980	-1.0136	1.3307	0.2651	0.7909
Acu:MiET	-0.0961	0.4847	-1.0462	0.8540	-0.1982	0.8429
Acu:MnT	0.3705	0.6028	-0.8110	1.5519	0.6146	0.5388
Acu:PlaSh	-0.0275	0.4079	-0.8269	0.7720	-0.0673	0.9463
Acu:Plt	-1.3020	0.7527	-2.7772	0.1732	-1.7299	0.0837
Acu:ReET	-0.4026	0.5498	-1.4801	0.6750	-0.7323	0.4640
Acu:rTMS	0.2099	0.5448	-0.8580	1.2777	0.3852	0.7001
Acu:tDCS	0.1653	0.5661	-0.9443	1.2748	0.2920	0.7703
Acu:WBV	-0.2767	0.8587	-1.9597	1.4063	-0.3223	0.7473
Acu:WlNi	-0.1761	0.4686	-1.0945	0.7423	-0.3758	0.7070
AeET:AqET	-0.2151	0.4376	-1.0728	0.6425	-0.4916	0.6230
AeET:Bal	0.2216	0.6542	-1.0607	1.5038	0.3387	0.7349
AeET:CBT	0.0648	0.3447	-0.6108	0.7405	0.1881	0.8508
AeET:Cry	-0.5936	1.6885	-3.9029	2.7157	-0.3516	0.7252
AeET:DryN	0.2706	0.5972	-0.8998	1.4410	0.4531	0.6504
AeET:Elec	0.1914	0.4854	-0.7600	1.1427	0.3942	0.6934
AeET:FlET	-0.1101	0.4251	-0.9433	0.7230	-0.2591	0.7955
AeET:HtT	2.0052	1.1531	-0.2548	4.2652	1.7390	0.0820
AeET:MasT	0.5160	0.4601	-0.3858	1.4177	1.1215	0.2621
AeET:McT	0.3383	0.3226	-0.2939	0.9705	1.0489	0.2942
AeET:MfT	0.6053	0.6072	-0.5849	1.7954	0.9968	0.3189
AeET:MiET	0.3506	0.3522	-0.3396	1.0409	0.9956	0.3195
AeET:MnT	0.8172	0.5983	-0.3554	1.9898	1.3659	0.1720
AeET:PlaSh	0.4192	0.4212	-0.4064	1.2448	0.9953	0.3196
AeET:Plt	-0.8553	0.6770	-2.1823	0.4717	-1.2633	0.2065
AeET:ReET	0.0441	0.3546	-0.6508	0.7391	0.1245	0.9010
AeET:rTMS	0.6566	0.5549	-0.4309	1.7442	1.1833	0.2367
AeET:tDCS	0.6120	0.5758	-0.5165	1.7405	1.0629	0.2878
AeET:WBV	0.1700	0.7953	-1.3888	1.7288	0.2138	0.8307
AeET:WlNi	0.2706	0.3415	-0.3987	0.9399	0.7924	0.4281
AqET:Bal	0.4367	0.6607	-0.8583	1.7316	0.6609	0.5087
AqET:CBT	0.2800	0.4284	-0.5596	1.1196	0.6535	0.5134
AqET:Cry	-0.3785	1.7074	-3.7249	2.9679	-0.2217	0.8246
AqET:DryN	0.4857	0.6488	-0.7859	1.7574	0.7486	0.4541
AqET:Elec	0.4065	0.5396	-0.6512	1.4641	0.7532	0.4513
AqET:FlET	0.1050	0.2853	-0.4542	0.6642	0.3679	0.7129
AqET:HtT	2.2203	1.2103	-0.1518	4.5925	1.8345	0.0666
AqET:MasT	0.7311	0.5023	-0.2533	1.7155	1.4556	0.1455
AqET:McT	0.5534	0.4266	-0.2827	1.3896	1.2973	0.1945
AqET:MfT	0.8204	0.6514	-0.4563	2.0971	1.2594	0.2079
AqET:MiET	0.5657	0.4419	-0.3003	1.4318	1.2803	0.2005
AqET:MnT	1.0323	0.6440	-0.2300	2.2946	1.6029	0.1090
AqET:PlaSh	0.6344	0.4827	-0.3118	1.5805	1.3141	0.1888
AqET:Plt	-0.6402	0.6572	-1.9283	0.6479	-0.9741	0.3300
AqET:ReET	0.2592	0.4112	-0.5467	1.0652	0.6305	0.5284
AqET:rTMS	0.8717	0.6029	-0.3100	2.0534	1.4459	0.1482
AqET:tDCS	0.8271	0.6222	-0.3924	2.0466	1.3293	0.1837
AqET:WBV	0.3851	0.8355	-1.2524	2.0226	0.4610	0.6448
AqET:WlNi	0.4857	0.4254	-0.3480	1.3194	1.1418	0.2535
Bal:CBT	-0.1567	0.5993	-1.3312	1.0178	-0.2615	0.7937
Bal:Cry	-0.8152	1.7578	-4.2604	2.6301	-0.4637	0.6428
Bal:DryN	0.0490	0.7718	-1.4637	1.5618	0.0635	0.9493
Bal:Elec	-0.0302	0.6905	-1.3835	1.3231	-0.0437	0.9651
Bal:FlET	-0.3317	0.6634	-1.6319	0.9685	-0.5000	0.6171
Bal:HtT	1.7837	1.2889	-0.7426	4.3099	1.3838	0.1664
Bal:MasT	0.2944	0.6735	-1.0256	1.6144	0.4372	0.6620
Bal:McT	0.1168	0.6015	-1.0621	1.2957	0.1941	0.8461
Bal:MfT	0.3837	0.7809	-1.1469	1.9143	0.4913	0.6232
Bal:MiET	0.1291	0.5886	-1.0246	1.2827	0.2193	0.8264
Bal:MnT	0.5956	0.7738	-0.9210	2.1123	0.7698	0.4414
Bal:PlaSh	0.1977	0.6470	-1.0704	1.4657	0.3055	0.7599
Bal:Plt	-1.0769	0.8518	-2.7464	0.5927	-1.2642	0.2062
Bal:ReET	-0.1774	0.6499	-1.4512	1.0963	-0.2730	0.7848
Bal:rTMS	0.4350	0.7410	-1.0172	1.8873	0.5871	0.5571
Bal:tDCS	0.3904	0.7567	-1.0928	1.8736	0.5159	0.6059
Bal:WBV	-0.0516	0.9345	-1.8831	1.7799	-0.0552	0.9560
Bal:WlNi	0.0490	0.5964	-1.1199	1.2180	0.0822	0.9345
CBT:Cry	-0.6584	1.6583	-3.9086	2.5917	-0.3971	0.6913
CBT:DryN	0.2058	0.5056	-0.7852	1.1967	0.4070	0.6840
CBT:Elec	0.1265	0.3748	-0.6081	0.8612	0.3375	0.7357
CBT:FlET	-0.1750	0.4022	-0.9634	0.6134	-0.4350	0.6635
CBT:HtT	1.9404	1.1649	-0.3427	4.2235	1.6657	0.0958
CBT:MasT	0.4511	0.3621	-0.2585	1.1608	1.2460	0.2128
CBT:McT	0.2735	0.1798	-0.0789	0.6259	1.5210	0.1283
CBT:MfT	0.5404	0.5231	-0.4848	1.5656	1.0332	0.3015
CBT:MiET	0.2858	0.2176	-0.1407	0.7122	1.3135	0.1890
CBT:MnT	0.7524	0.5118	-0.2508	1.7555	1.4700	0.1416
CBT:PlaSh	0.3544	0.2869	-0.2080	0.9168	1.2352	0.2168
CBT:Plt	-0.9201	0.6563	-2.2065	0.3663	-1.4019	0.1609
CBT:ReET	-0.0207	0.3307	-0.6688	0.6274	-0.0626	0.9501
CBT:rTMS	0.5918	0.4613	-0.3123	1.4959	1.2829	0.1995
CBT:tDCS	0.5472	0.4862	-0.4058	1.5001	1.1253	0.2605
CBT:WBV	0.1052	0.7308	-1.3272	1.5375	0.1439	0.8856
CBT:WlNi	0.2058	0.1250	-0.0392	0.4507	1.6462	0.0997
Cry:DryN	0.8642	1.7246	-2.5159	4.2444	0.5011	0.6163
Cry:Elec	0.7850	1.6974	-2.5418	4.1118	0.4625	0.6438
Cry:FlET	0.4835	1.7011	-2.8506	3.8175	0.2842	0.7762
Cry:HtT	2.5988	2.0218	-1.3638	6.5615	1.2854	0.1987
Cry:MasT	1.1096	1.6937	-2.2099	4.4291	0.6551	0.5124
Cry:McT	0.9319	1.6624	-2.3263	4.1902	0.5606	0.5751
Cry:MfT	1.1989	1.7361	-2.2039	4.6016	0.6905	0.4899
Cry:MiET	0.9442	1.6663	-2.3217	4.2102	0.5667	0.5710
Cry:MnT	1.4108	1.7319	-1.9836	4.8052	0.8146	0.4153
Cry:PlaSh	1.0129	1.6801	-2.2802	4.3059	0.6028	0.5466
Cry:Plt	-0.2617	1.7791	-3.7487	3.2254	-0.1471	0.8831
Cry:ReET	0.6377	1.6863	-2.6673	3.9427	0.3782	0.7053
Cry:rTMS	1.2502	1.7185	-2.1180	4.6185	0.7275	0.4669
Cry:tDCS	1.2056	1.7254	-2.1761	4.5873	0.6987	0.4847
Cry:WBV	0.7636	1.8038	-2.7718	4.2990	0.4233	0.6721
Cry:WlNi	0.8642	1.6536	-2.3767	4.1051	0.5226	0.6012
DryN:Elec	-0.0792	0.6219	-1.2982	1.1397	-0.1274	0.8986
DryN:FlET	-0.3807	0.6320	-1.6194	0.8579	-0.6025	0.5469
DryN:HtT	1.7346	1.2623	-0.7395	4.2087	1.3742	0.1694
DryN:MasT	0.2454	0.6117	-0.9536	1.4443	0.4011	0.6883
DryN:McT	0.0677	0.5190	-0.9496	1.0850	0.1305	0.8962
DryN:MfT	0.3347	0.7211	-1.0786	1.7479	0.4641	0.6426
DryN:MiET	0.0800	0.5314	-0.9615	1.1216	0.1506	0.8803
DryN:MnT	0.5466	0.7107	-0.8464	1.9395	0.7691	0.4418
DryN:PlaSh	0.1486	0.5733	-0.9750	1.2722	0.2593	0.7954
DryN:Plt	-1.1259	0.8192	-2.7315	0.4797	-1.3744	0.1693
DryN:ReET	-0.2265	0.5909	-1.3847	0.9317	-0.3832	0.7015
DryN:rTMS	0.3860	0.6776	-0.9420	1.7140	0.5697	0.5689
DryN:tDCS	0.3414	0.6948	-1.0204	1.7031	0.4914	0.6232
DryN:WBV	-0.1006	0.8715	-1.8087	1.6075	-0.1154	0.9081
DryN:WlNi	0.0000	0.4899	-0.9602	0.9602	0.0000	1.0000
Elec:FlET	-0.3015	0.5204	-1.3215	0.7185	-0.5793	0.5624
Elec:HtT	1.8139	1.2133	-0.5642	4.1919	1.4950	0.1349
Elec:MasT	0.3246	0.3902	-0.4402	1.0894	0.8319	0.4055
Elec:McT	0.1470	0.3931	-0.6234	0.9174	0.3739	0.7085
Elec:MfT	0.4139	0.4994	-0.5650	1.3928	0.8287	0.4073
Elec:MiET	0.1593	0.4010	-0.6266	0.9452	0.3972	0.6912
Elec:MnT	0.6258	0.5102	-0.3741	1.6258	1.2267	0.2199
Elec:PlaSh	0.2279	0.2412	-0.2448	0.7006	0.9449	0.3447
Elec:Plt	-1.0467	0.7047	-2.4279	0.3346	-1.4852	0.1375
Elec:ReET	-0.1472	0.4791	-1.0863	0.7918	-0.3073	0.7586
Elec:rTMS	0.4652	0.4343	-0.3860	1.3165	1.0712	0.2841
Elec:tDCS	0.4206	0.4607	-0.4823	1.3236	0.9130	0.3612
Elec:WBV	-0.0214	0.8151	-1.6190	1.5763	-0.0262	0.9791
Elec:WlNi	0.0792	0.3832	-0.6717	0.8302	0.2068	0.8362
FlET:HtT	2.1154	1.2042	-0.2448	4.4755	1.7567	0.0790
FlET:MasT	0.6261	0.4828	-0.3201	1.5724	1.2969	0.1947
FlET:McT	0.4485	0.4015	-0.3385	1.2355	1.1169	0.2640
FlET:MfT	0.7154	0.6356	-0.5303	1.9611	1.1256	0.2603
FlET:MiET	0.4608	0.4196	-0.3616	1.2831	1.0982	0.2721
FlET:MnT	0.9273	0.6279	-0.3033	2.1580	1.4769	0.1397
FlET:PlaSh	0.5294	0.4612	-0.3745	1.4333	1.1479	0.2510
FlET:Plt	-0.7451	0.6718	-2.0619	0.5717	-1.1091	0.2674
FlET:ReET	0.1543	0.3608	-0.5528	0.8613	0.4277	0.6689
FlET:rTMS	0.7668	0.5858	-0.3814	1.9149	1.3089	0.1906
FlET:tDCS	0.7221	0.6056	-0.4648	1.9091	1.1924	0.2331
FlET:WBV	0.2801	0.8225	-1.3320	1.8923	0.3406	0.7334
FlET:WlNi	0.3807	0.3993	-0.4018	1.1633	0.9536	0.3403
HtT:MasT	-1.4892	1.2038	-3.8486	0.8702	-1.2371	0.2160
HtT:McT	-1.6669	1.1605	-3.9414	0.6076	-1.4364	0.1509
HtT:MfT	-1.4000	1.2670	-3.8832	1.0833	-1.1050	0.2692
HtT:MiET	-1.6546	1.1565	-3.9213	0.6121	-1.4307	0.1525
HtT:MnT	-1.1880	1.2627	-3.6629	1.2869	-0.9408	0.3468
HtT:PlaSh	-1.5860	1.1891	-3.9166	0.7446	-1.3338	0.1823
HtT:Plt	-2.8605	1.3119	-5.4319	-0.2891	-2.1804	0.0292
HtT:ReET	-1.9611	1.1824	-4.2785	0.3563	-1.6586	0.0972
HtT:rTMS	-1.3486	1.2427	-3.7843	1.0871	-1.0852	0.2778
HtT:tDCS	-1.3932	1.2522	-3.8475	1.0611	-1.1126	0.2659
HtT:WBV	-1.8352	1.3674	-4.5153	0.8449	-1.3421	0.1796
HtT:WlNi	-1.7346	1.1634	-4.0148	0.5456	-1.4910	0.1360
MasT:McT	-0.1777	0.3742	-0.9110	0.5557	-0.4748	0.6349
MasT:MfT	0.0893	0.5342	-0.9577	1.1363	0.1671	0.8673
MasT:MiET	-0.1654	0.3726	-0.8957	0.5650	-0.4437	0.6572
MasT:MnT	0.3012	0.5378	-0.7528	1.3552	0.5601	0.5754
MasT:PlaSh	-0.0967	0.3068	-0.6980	0.5045	-0.3154	0.7525
MasT:Plt	-1.3713	0.6351	-2.6160	-0.1265	-2.1592	0.0308
MasT:ReET	-0.4718	0.4537	-1.3611	0.4174	-1.0400	0.2984
MasT:rTMS	0.1406	0.4739	-0.7881	1.0694	0.2968	0.7666
MasT:tDCS	0.0960	0.4982	-0.8804	1.0724	0.1927	0.8472
MasT:WBV	-0.3460	0.8073	-1.9282	1.2362	-0.4286	0.6682
MasT:WlNi	-0.2454	0.3663	-0.9634	0.4726	-0.6698	0.5030
McT:MfT	0.2669	0.5363	-0.7842	1.3180	0.4977	0.6187
McT:MiET	0.0123	0.2058	-0.3911	0.4157	0.0597	0.9524
McT:MnT	0.4789	0.5253	-0.5507	1.5084	0.9116	0.3620
McT:PlaSh	0.0809	0.3104	-0.5274	0.6892	0.2607	0.7943
McT:Plt	-1.1936	0.6578	-2.4829	0.0957	-1.8145	0.0696
McT:ReET	-0.2942	0.3263	-0.9338	0.3454	-0.9015	0.3673
McT:rTMS	0.3183	0.4762	-0.6151	1.2517	0.6683	0.5039
McT:tDCS	0.2737	0.5004	-0.7071	1.2545	0.5469	0.5845
McT:WBV	-0.1683	0.7374	-1.6137	1.2770	-0.2283	0.8194
McT:WlNi	-0.0677	0.1714	-0.4037	0.2683	-0.3950	0.6928
MfT:MiET	-0.2546	0.5421	-1.3172	0.8079	-0.4697	0.6386
MfT:MnT	0.2119	0.6272	-1.0174	1.4412	0.3379	0.7354
MfT:PlaSh	-0.1860	0.4374	-1.0432	0.6712	-0.4253	0.6706
MfT:Plt	-1.4606	0.7936	-3.0159	0.0948	-1.8405	0.0657
MfT:ReET	-0.5611	0.6022	-1.7414	0.6192	-0.9318	0.3515
MfT:rTMS	0.0513	0.5672	-1.0604	1.1631	0.0905	0.9279
MfT:tDCS	0.0067	0.5877	-1.1451	1.1586	0.0115	0.9909
MfT:WBV	-0.4353	0.8931	-2.1856	1.3151	-0.4874	0.6260
MfT:WlNi	-0.3347	0.5291	-1.3716	0.7023	-0.6325	0.5270
MiET:MnT	0.4666	0.5321	-0.5764	1.5096	0.8768	0.3806
MiET:PlaSh	0.0686	0.3203	-0.5592	0.6965	0.2142	0.8304
MiET:Plt	-1.2059	0.6631	-2.5056	0.0937	-1.8186	0.0690
MiET:ReET	-0.3065	0.3567	-1.0056	0.3926	-0.8592	0.3902
MiET:rTMS	0.3060	0.4828	-0.6402	1.2522	0.6338	0.5262
MiET:tDCS	0.2614	0.5067	-0.7316	1.2544	0.5159	0.6059
MiET:WBV	-0.1806	0.7479	-1.6465	1.2853	-0.2415	0.8092
MiET:WlNi	-0.0800	0.2059	-0.4836	0.3235	-0.3886	0.6976
MnT:PlaSh	-0.3980	0.4496	-1.2791	0.4832	-0.8852	0.3760
MnT:Plt	-1.6725	0.7916	-3.2241	-0.1209	-2.1127	0.0346
MnT:ReET	-0.7731	0.5930	-1.9353	0.3892	-1.3036	0.1924
MnT:rTMS	-0.1606	0.5767	-1.2909	0.9697	-0.2785	0.7806
MnT:tDCS	-0.2052	0.5968	-1.3749	0.9645	-0.3438	0.7310
MnT:WBV	-0.6472	0.8849	-2.3815	1.0871	-0.7314	0.4645
MnT:WlNi	-0.5466	0.5149	-1.5557	0.4625	-1.0616	0.2884
PlaSh:Plt	-1.2745	0.6622	-2.5724	0.0233	-1.9248	0.0543
PlaSh:ReET	-0.3751	0.4140	-1.1865	0.4363	-0.9061	0.3649
PlaSh:rTMS	0.2374	0.3612	-0.4705	0.9453	0.6572	0.5111
PlaSh:tDCS	0.1927	0.3925	-0.5766	0.9621	0.4910	0.6234
PlaSh:WBV	-0.2492	0.7786	-1.7754	1.2769	-0.3201	0.7489
PlaSh:WlNi	-0.1486	0.2977	-0.7322	0.4349	-0.4992	0.6176
Plt:ReET	0.8994	0.6808	-0.4349	2.2337	1.3212	0.1864
Plt:rTMS	1.5119	0.7543	0.0335	2.9903	2.0044	0.0450
Plt:tDCS	1.4673	0.7698	-0.0415	2.9760	1.9061	0.0566
Plt:WBV	1.0253	0.9738	-0.8832	2.9338	1.0529	0.2924
Plt:WlNi	1.1259	0.6566	-0.1609	2.4127	1.7148	0.0864
ReET:rTMS	0.6125	0.5494	-0.4643	1.6893	1.1148	0.2649
ReET:tDCS	0.5679	0.5705	-0.5503	1.6860	0.9954	0.3195
ReET:WBV	0.1259	0.7912	-1.4248	1.6765	0.1591	0.8736
ReET:WlNi	0.2265	0.3304	-0.4212	0.8741	0.6854	0.4931
rTMS:tDCS	-0.0446	0.5334	-1.0901	1.0009	-0.0836	0.9333
rTMS:WBV	-0.4866	0.8583	-2.1689	1.1957	-0.5669	0.5708
rTMS:WlNi	-0.3860	0.4681	-1.3034	0.5314	-0.8246	0.4096
tDCS:WBV	-0.4420	0.8720	-2.1511	1.2671	-0.5069	0.6122
tDCS:WlNi	-0.3414	0.4927	-1.3070	0.6242	-0.6929	0.4884
WBV:WlNi	0.1006	0.7208	-1.3121	1.5133	0.1396	0.8890

Code

# Forest plot of node-splitting
plot_netsplit_forest(split_sr_ash)

Figure 13: Forest plot of node-splitting for Subnet 1 (A | Sh)

Code

# Heatmap
netheat(nma_sr_ash)

Heatmap of contribution matrix for Subnet 1 (A | Sh)

Code

# Funnel plot
funnel(nma_sr_ash,
    order = "WlNi",
)