create_dt <- function(x){
DT::datatable(x,
extensions = 'Buttons',
options = list(dom = 'Blfrtip',
buttons = c('copy', 'csv', 'excel', 'pdf', 'print'),
lengthMenu = list(c(10,25,50,-1),
c(10,25,50,"All"))))
}
# Load Data
data <- read_xlsx("New Data Pedicle screw.xlsx") %>%
mutate(trt = fct_recode(trt, CT_Nav = "ct", FFG = "fluoro", RA = "robo"))
create_dt(data)
# Clean and Nest Data by Outcome
dat <- data %>%
mutate(
names = rep(c("1" , "2"), times = 42)) %>%
pivot_wider(
names_from = names,
values_from = c(trt, n, mean, sd, sex, age),
id_cols = c(1:3)) %>%
na.omit() %>%
group_by(outcome) %>%
nest()
create_dt(dat)
res <- dat %>%
mutate(
# Calculate Mean Difference & Standard Error Between Treatment and Control groups in Each Study for Each Outcome
pairwise = map(
.x = data,
~pairwise(
treat= list(trt_1, trt_2), n = list(n_1, n_2),
mean= list(mean_1, mean_2), sd= list(sd_1, sd_2),
studlab = auth, data = .x, sm = "SMD", reference.group = "2")),
# Perform a Random-Effects Network Meta-Analysis for each Outcome Using Mean Differences and Standard Errors from Pairwise Comparisons
net = map(
.x = pairwise,
~netmeta(
.x,
random = TRUE,
common = FALSE,
reference.group = "FFG",
sm = "SMD",
details.chkmultiarm = TRUE,
sep.trts = " vs. ")),
# Effect Table
effect.table = map(
.x = net,
~netleague(.x,
bracket = "(", # use round brackets
digits=2)),
# Show Results for Direct and Indirect Evidence
split = map(
.x = net,
~netsplit(.x)),
# Calculate Total Inconsistency based on the full design-by-treatment interaction random-effects model
incon = map(
.x = net,
~decomp.design(.x))
)
# Create Labels
# Treatment Order
long.labels <- c("CT-navigated", "Fluoroscopy-guided", "Robot-assisted")
# Outcome Order
outcome.labels1 <- c("Length of Stay (days)",
"Operation Time (minutes)",
"Oswestry Disability Index",
"Visual Analog Scale: Back",
"Visual Analog Scale: Leg",
"Blood Loss (mL)")
outcome.labels2 <- c("Length of Stay",
"Operation Time",
"Oswestry Disability Index",
"Visual Analog Scale: Back",
"Visual Analog Scale: Leg",
"Blood Loss")
Tables of Comparisons Between Treatments for All Outcomes
tab.list <- list()
for(i in 1:nrow(res)){
res.tab <- res$split[[i]]$random
tab.list[[i]] <- res.tab %>%
mutate(across(where(is.numeric), round, 2)) %>%
mutate("95% CI" = paste(lower, upper, sep = " to "),
Evidence = c("Direct", "Indirect", "Direct"),
SMD = TE,
SE = seTE,
p.value = p) %>%
select(-c(statistic, lower, upper)) %>%
select(comparison, Evidence, SMD, SE, "95% CI", p.value)
}
## Warning: There was 1 warning in `mutate()`.
## ℹ In argument: `across(where(is.numeric), round, 2)`.
## Caused by warning:
## ! The `...` argument of `across()` is deprecated as of dplyr 1.1.0.
## Supply arguments directly to `.fns` through an anonymous function instead.
##
## # Previously
## across(a:b, mean, na.rm = TRUE)
##
## # Now
## across(a:b, \(x) mean(x, na.rm = TRUE))
names(tab.list) <- outcome.labels2
Length of Stay
Standardized Mean Difference
create_dt(tab.list[[1]])
Operation Time
create_dt(tab.list[[2]])
Oswestry Disability Index
create_dt(tab.list[[3]])
Visual Analog Scale: Back
create_dt(tab.list[[4]])
Visual Analog Scale: Leg
create_dt(tab.list[[5]])
Blood Loss
create_dt(tab.list[[6]])
Forest Plots for SMD’s
for(i in 1:nrow(res)){
forest(res$net[[i]],
ref= c("RA", "CT_Nav"),
xlim= c(-4, 3),
baseline= FALSE,
drop= TRUE,
pooled = "random",
smlab = outcome.labels2[i],
labels = long.labels,
label.left = "Favors Treatment",
label.right = "Favors 'Other'")
}
P.Scores
plot(
netrank(res$net[[1]]),
netrank(res$net[[2]]),
netrank(res$net[[3]]),
netrank(res$net[[4]]),
netrank(res$net[[5]]),
netrank(res$net[[6]]),
name = outcome.labels2,
digits = 2)
Mean Differences for Length of Stay and Operation
Time
res.md <- dat %>%
#filter(outcome %in% c("LOS", "OP_Time")) %>%
mutate(
# Calculate Mean Difference & Standard Error Between Treatment and Control groups in Each Study for Each Outcome
pairwise = map(
.x = data,
~pairwise(
treat= list(trt_1, trt_2), n = list(n_1, n_2),
mean= list(mean_1, mean_2), sd= list(sd_1, sd_2),
studlab = auth, data = .x, sm = "MD", reference.group = "2")),
# Perform a Random-Effects Network Meta-Analysis for each Outcome Using Mean Differences and Standard Errors from Pairwise Comparisons
net = map(
.x = pairwise,
~netmeta(
.x,
random = TRUE,
common = FALSE,
reference.group = "FFG",
sm = "MD",
details.chkmultiarm = TRUE,
sep.trts = " vs. ")),
# Show Results for Direct and Indirect Evidence
split = map(
.x = net,
~netsplit(.x)),
# Calculate Total Inconsistency based on the full design-by-treatment interaction random-effects model
incon = map(
.x = net,
~decomp.design(.x))
)
Table of Comparisons for Mean Differences in Length of Stay
and Operation Time
md.list <- list()
for(i in 1:nrow(res.md)){
md.tab <- res.md$split[[i]]$random
md.list[[i]] <- md.tab %>%
mutate(across(where(is.numeric), round, 2)) %>%
mutate("95% CI" = paste(lower, upper, sep = " to "),
Evidence = c("Direct", "Indirect", "Direct"),
MD = TE,
SE = seTE,
p.value = p) %>%
select(-c(statistic, lower, upper)) %>%
select(comparison, Evidence, MD, SE, "95% CI", p.value)
}
Table of Comparisons for Length of Stay (MD)
create_dt(md.list[[1]])
Table of Comparisons for Operation Time (MD)
create_dt(md.list[[2]])
Table of Comparisons for ODI (MD)
create_dt(md.list[[3]])
Table of Comparisons for VAS: Back (MD)
create_dt(md.list[[4]])
Table of Comparisons for VAS: Leg (MD)
create_dt(md.list[[5]])
Table of Comparisons for Blood Loss (MD)
create_dt(md.list[[6]])
Forest Plots for MD’s
Length of Stay
los.forest.md <- forest(res.md$net[[1]],
ref= c("RA", "CT_Nav"),
xlim= c(-4, 4),
baseline= FALSE,
drop= TRUE,
pooled = "random",
smlab = outcome.labels1[1],
labels = long.labels,
label.left = "Favors Treatment",
label.right = "Favors 'Other'")
Operation Time
op.t.forest.md <- forest(res.md$net[[2]],
ref= c("RA", "CT_Nav"),
xlim= c(-15, 15),
baseline= FALSE,
drop= TRUE,
pooled = "random",
smlab = outcome.labels1[2],
labels = long.labels,
label.left = "Favors Treatment",
label.right = "Favors 'Other'")
ODI
ODI.forest.md <- forest(res.md$net[[3]],
ref= c("RA", "CT_Nav"),
xlim= c(-3, 3),
baseline= FALSE,
drop= TRUE,
pooled = "random",
smlab = outcome.labels1[3],
labels = long.labels,
label.left = "Favors Treatment",
label.right = "Favors 'Other'")
VAS: Back
VAS.back.forest.md <- forest(res.md$net[[4]],
ref= c("RA", "CT_Nav"),
xlim= c(-1, 1),
baseline= FALSE,
drop= TRUE,
pooled = "random",
smlab = outcome.labels1[4],
labels = long.labels,
label.left = "Favors Treatment",
label.right = "Favors 'Other'")
VAS: Leg
VAS.leg.forest.md <- forest(res.md$net[[5]],
ref= c("RA", "CT_Nav"),
xlim= c(-1, 1),
baseline= FALSE,
drop= TRUE,
pooled = "random",
smlab = outcome.labels1[5],
labels = long.labels,
label.left = "Favors Treatment",
label.right = "Favors 'Other'")
Blood Loss
blood.loss.forest.md <- forest(res.md$net[[6]],
ref= c("RA", "CT_Nav"),
xlim= c(-200, 100),
baseline= FALSE,
drop= TRUE,
pooled = "random",
smlab = outcome.labels1[6],
labels = long.labels,
label.left = "Favors Treatment",
label.right = "Favors 'Other'")
lapply(res.md$net, summary)
## [[1]]
## Original data:
##
## treat1 treat2 TE seTE
## Chen CT_Nav FFG -3.2200 0.9659
## Cui FFG RA 2.7000 0.4932
## Hyun FFG RA 2.6000 1.0578
## Tian CT_Nav FFG -1.0500 0.3084
## Wang CT_Nav FFG -0.5300 0.7592
## Wu CT_Nav FFG -0.2100 0.6738
##
## Number of treatment arms (by study):
## narms
## Chen 2
## Cui 2
## Hyun 2
## Tian 2
## Wang 2
## Wu 2
##
## Results (random effects model):
##
## treat1 treat2 MD 95%-CI
## Chen CT_Nav FFG -1.0632 [-1.8973; -0.2292]
## Cui FFG RA 2.6717 [ 1.4154; 3.9279]
## Hyun FFG RA 2.6717 [ 1.4154; 3.9279]
## Tian CT_Nav FFG -1.0632 [-1.8973; -0.2292]
## Wang CT_Nav FFG -1.0632 [-1.8973; -0.2292]
## Wu CT_Nav FFG -1.0632 [-1.8973; -0.2292]
##
## Number of studies: k = 6
## Number of pairwise comparisons: m = 6
## Number of observations: o = 302
## Number of treatments: n = 3
## Number of designs: d = 2
##
## Random effects model
##
## Treatment estimate (sm = 'MD', comparison: other treatments vs 'FFG'):
## MD 95%-CI z p-value
## CT_Nav -1.0632 [-1.8973; -0.2292] -2.50 0.0125
## FFG . . . .
## RA -2.6717 [-3.9279; -1.4154] -4.17 < 0.0001
##
## Quantifying heterogeneity / inconsistency:
## tau^2 = 0.3301; tau = 0.5745; I^2 = 43.4% [0.0%; 79.2%]
##
## Tests of heterogeneity (within designs) and inconsistency (between designs):
## Q d.f. p-value
## Total 7.07 4 0.1324
## Within designs 7.07 4 0.1324
## Between designs 0.00 0 --
##
## [[2]]
## Original data:
##
## treat1 treat2 TE seTE
## Chen CT_Nav FFG 87.8300 8.0816
## Cui FFG RA -32.9000 2.7332
## Feng FFG RA 34.3800 13.2115
## Hyun FFG RA -0.0000 16.6885
## Tian CT_Nav FFG 46.1400 5.5535
## Wang CT_Nav FFG -32.6400 12.2223
## Wu CT_Nav FFG -47.1300 9.3536
##
## Number of treatment arms (by study):
## narms
## Chen 2
## Cui 2
## Feng 2
## Hyun 2
## Tian 2
## Wang 2
## Wu 2
##
## Results (random effects model):
##
## treat1 treat2 MD 95%-CI
## Chen CT_Nav FFG 14.0835 [-39.2563; 67.4232]
## Cui FFG RA -0.1401 [-62.4212; 62.1411]
## Feng FFG RA -0.1401 [-62.4212; 62.1411]
## Hyun FFG RA -0.1401 [-62.4212; 62.1411]
## Tian CT_Nav FFG 14.0835 [-39.2563; 67.4232]
## Wang CT_Nav FFG 14.0835 [-39.2563; 67.4232]
## Wu CT_Nav FFG 14.0835 [-39.2563; 67.4232]
##
## Number of studies: k = 7
## Number of pairwise comparisons: m = 7
## Number of observations: o = 382
## Number of treatments: n = 3
## Number of designs: d = 2
##
## Random effects model
##
## Treatment estimate (sm = 'MD', comparison: other treatments vs 'FFG'):
## MD 95%-CI z p-value
## CT_Nav 14.0835 [-39.2563; 67.4232] 0.52 0.6048
## FFG . . . .
## RA 0.1401 [-62.1411; 62.4212] 0.00 0.9965
##
## Quantifying heterogeneity / inconsistency:
## tau^2 = 2879.9214; tau = 53.6649; I^2 = 97.2% [95.7%; 98.2%]
##
## Tests of heterogeneity (within designs) and inconsistency (between designs):
## Q d.f. p-value
## Total 181.79 5 < 0.0001
## Within designs 181.79 5 < 0.0001
## Between designs 0.00 0 --
##
## [[3]]
## Original data:
##
## treat1 treat2 TE seTE
## Chen CT_Nav FFG -0.4900 0.4341
## Cui FFG RA 1.6000 1.1691
## Feng FFG RA 0.3500 1.1383
## Wang CT_Nav FFG 0.2700 0.8780
## Wu CT_Nav FFG 0.1500 3.0025
##
## Number of treatment arms (by study):
## narms
## Chen 2
## Cui 2
## Feng 2
## Wang 2
## Wu 2
##
## Results (random effects model):
##
## treat1 treat2 MD 95%-CI
## Chen CT_Nav FFG -0.3326 [-1.0890; 0.4239]
## Cui FFG RA 0.9583 [-0.6402; 2.5569]
## Feng FFG RA 0.9583 [-0.6402; 2.5569]
## Wang CT_Nav FFG -0.3326 [-1.0890; 0.4239]
## Wu CT_Nav FFG -0.3326 [-1.0890; 0.4239]
##
## Number of studies: k = 5
## Number of pairwise comparisons: m = 5
## Number of observations: o = 261
## Number of treatments: n = 3
## Number of designs: d = 2
##
## Random effects model
##
## Treatment estimate (sm = 'MD', comparison: other treatments vs 'FFG'):
## MD 95%-CI z p-value
## CT_Nav -0.3326 [-1.0890; 0.4239] -0.86 0.3888
## FFG . . . .
## RA -0.9583 [-2.5569; 0.6402] -1.17 0.2400
##
## Quantifying heterogeneity / inconsistency:
## tau^2 = 0; tau = 0; I^2 = 0% [0.0%; 84.7%]
##
## Tests of heterogeneity (within designs) and inconsistency (between designs):
## Q d.f. p-value
## Total 1.22 3 0.7494
## Within designs 1.22 3 0.7494
## Between designs 0.00 0 --
##
## [[4]]
## Original data:
##
## treat1 treat2 TE seTE
## Chen CT_Nav FFG -0.3900 0.2327
## Cui FFG RA 0.3000 0.1890
## Feng FFG RA 0.0300 0.1664
## Hyun FFG RA 1.1000 0.4535
## Wang CT_Nav FFG -0.0800 0.2234
##
## Number of treatment arms (by study):
## narms
## Chen 2
## Cui 2
## Feng 2
## Hyun 2
## Wang 2
##
## Results (random effects model):
##
## treat1 treat2 MD 95%-CI
## Chen CT_Nav FFG -0.2319 [-0.6853; 0.2215]
## Cui FFG RA 0.2928 [-0.0848; 0.6704]
## Feng FFG RA 0.2928 [-0.0848; 0.6704]
## Hyun FFG RA 0.2928 [-0.0848; 0.6704]
## Wang CT_Nav FFG -0.2319 [-0.6853; 0.2215]
##
## Number of studies: k = 5
## Number of pairwise comparisons: m = 5
## Number of observations: o = 274
## Number of treatments: n = 3
## Number of designs: d = 2
##
## Random effects model
##
## Treatment estimate (sm = 'MD', comparison: other treatments vs 'FFG'):
## MD 95%-CI z p-value
## CT_Nav -0.2319 [-0.6853; 0.2215] -1.00 0.3161
## FFG . . . .
## RA -0.2928 [-0.6704; 0.0848] -1.52 0.1286
##
## Quantifying heterogeneity / inconsistency:
## tau^2 = 0.0550; tau = 0.2346; I^2 = 51.4% [0.0%; 83.9%]
##
## Tests of heterogeneity (within designs) and inconsistency (between designs):
## Q d.f. p-value
## Total 6.17 3 0.1036
## Within designs 6.17 3 0.1036
## Between designs 0.00 0 --
##
## [[5]]
## Original data:
##
## treat1 treat2 TE seTE
## Chen CT_Nav FFG -0.1200 0.2296
## Feng FFG RA -0.0300 0.1811
## Hyun FFG RA 0.6000 0.7618
## Wang CT_Nav FFG 0.1200 0.2215
##
## Number of treatment arms (by study):
## narms
## Chen 2
## Feng 2
## Hyun 2
## Wang 2
##
## Results (random effects model):
##
## treat1 treat2 MD 95%-CI
## Chen CT_Nav FFG 0.0043 [-0.3082; 0.3167]
## Feng FFG RA 0.0037 [-0.3417; 0.3491]
## Hyun FFG RA 0.0037 [-0.3417; 0.3491]
## Wang CT_Nav FFG 0.0043 [-0.3082; 0.3167]
##
## Number of studies: k = 4
## Number of pairwise comparisons: m = 4
## Number of observations: o = 226
## Number of treatments: n = 3
## Number of designs: d = 2
##
## Random effects model
##
## Treatment estimate (sm = 'MD', comparison: other treatments vs 'FFG'):
## MD 95%-CI z p-value
## CT_Nav 0.0043 [-0.3082; 0.3167] 0.03 0.9786
## FFG . . . .
## RA -0.0037 [-0.3491; 0.3417] -0.02 0.9832
##
## Quantifying heterogeneity / inconsistency:
## tau^2 = 0; tau = 0; I^2 = 0% [0.0%; 89.6%]
##
## Tests of heterogeneity (within designs) and inconsistency (between designs):
## Q d.f. p-value
## Total 1.21 2 0.5452
## Within designs 1.21 2 0.5452
## Between designs 0.00 0 --
##
## [[6]]
## Original data:
##
## treat1 treat2 TE seTE
## Chen CT_Nav FFG -176.9100 11.4643
## Cui FFG RA 158.5000 6.0017
## Feng FFG RA 72.5000 31.0066
## Tian CT_Nav FFG -89.0200 23.7073
## Wang CT_Nav FFG -15.6000 88.2060
## Wu CT_Nav FFG -24.8800 50.2541
##
## Number of treatment arms (by study):
## narms
## Chen 2
## Cui 2
## Feng 2
## Tian 2
## Wang 2
## Wu 2
##
## Results (random effects model):
##
## treat1 treat2 MD 95%-CI
## Chen CT_Nav FFG -95.9597 [-171.3418; -20.5776]
## Cui FFG RA 119.7557 [ 25.4598; 214.0516]
## Feng FFG RA 119.7557 [ 25.4598; 214.0516]
## Tian CT_Nav FFG -95.9597 [-171.3418; -20.5776]
## Wang CT_Nav FFG -95.9597 [-171.3418; -20.5776]
## Wu CT_Nav FFG -95.9597 [-171.3418; -20.5776]
##
## Number of studies: k = 6
## Number of pairwise comparisons: m = 6
## Number of observations: o = 322
## Number of treatments: n = 3
## Number of designs: d = 2
##
## Random effects model
##
## Treatment estimate (sm = 'MD', comparison: other treatments vs 'FFG'):
## MD 95%-CI z p-value
## CT_Nav -95.9597 [-171.3418; -20.5776] -2.49 0.0126
## FFG . . . .
## RA -119.7557 [-214.0516; -25.4598] -2.49 0.0128
##
## Quantifying heterogeneity / inconsistency:
## tau^2 = 4176.4225; tau = 64.6252; I^2 = 85.7% [68.5%; 93.5%]
##
## Tests of heterogeneity (within designs) and inconsistency (between designs):
## Q d.f. p-value
## Total 27.97 4 < 0.0001
## Within designs 27.97 4 < 0.0001
## Between designs 0.00 0 --
map(.x = res.md$net,
~funnel(.x,
order = c("FFG", "CT_Nav", "RA"),
method.bias = "Egger",
digits.pval = 2))
## [[1]]
## studlab treat1 treat2 comparison TE TE.direct TE.adj seTE pch
## 1 Chen FFG CT_Nav FFG vs. CT_Nav 3.22 1.06324 2.15676003 0.9658696 1
## 2 Tian FFG CT_Nav FFG vs. CT_Nav 1.05 1.06324 -0.01323997 0.3084352 1
## 3 Wang FFG CT_Nav FFG vs. CT_Nav 0.53 1.06324 -0.53323997 0.7591582 1
## 4 Wu FFG CT_Nav FFG vs. CT_Nav 0.21 1.06324 -0.85323997 0.6737769 1
## 5 Cui FFG RA FFG vs. RA 2.70 2.67165 0.02834987 0.4932236 2
## 6 Hyun FFG RA FFG vs. RA 2.60 2.67165 -0.07165013 1.0578280 2
## col
## 1 black
## 2 black
## 3 black
## 4 black
## 5 black
## 6 black
##
## [[2]]
## studlab treat1 treat2 comparison TE TE.direct TE.adj seTE
## 1 Chen FFG CT_Nav FFG vs. CT_Nav -87.83 -14.083459 -73.746541 8.081595
## 2 Tian FFG CT_Nav FFG vs. CT_Nav -46.14 -14.083459 -32.056541 5.553506
## 3 Wang FFG CT_Nav FFG vs. CT_Nav 32.64 -14.083459 46.723459 12.222282
## 4 Wu FFG CT_Nav FFG vs. CT_Nav 47.13 -14.083459 61.213459 9.353600
## 5 Cui FFG RA FFG vs. RA -32.90 -0.140065 -32.759935 2.733187
## 6 Feng FFG RA FFG vs. RA 34.38 -0.140065 34.520065 13.211478
## 7 Hyun FFG RA FFG vs. RA 0.00 -0.140065 0.140065 16.688459
## pch col
## 1 1 black
## 2 1 black
## 3 1 black
## 4 1 black
## 5 2 black
## 6 2 black
## 7 2 black
##
## [[3]]
## studlab treat1 treat2 comparison TE TE.direct TE.adj seTE pch
## 1 Chen FFG CT_Nav FFG vs. CT_Nav 0.49 0.3325787 0.1574213 0.4341494 1
## 2 Wang FFG CT_Nav FFG vs. CT_Nav -0.27 0.3325787 -0.6025787 0.8780069 1
## 3 Wu FFG CT_Nav FFG vs. CT_Nav -0.15 0.3325787 -0.4825787 3.0025325 1
## 4 Cui FFG RA FFG vs. RA 1.60 0.9583125 0.6416875 1.1691468 2
## 5 Feng FFG RA FFG vs. RA 0.35 0.9583125 -0.6083125 1.1383365 2
## col
## 1 black
## 2 black
## 3 black
## 4 black
## 5 black
##
## [[4]]
## studlab treat1 treat2 comparison TE TE.direct TE.adj seTE
## 1 Chen FFG CT_Nav FFG vs. CT_Nav 0.39 0.2319281 0.158071924 0.2327015
## 2 Wang FFG CT_Nav FFG vs. CT_Nav 0.08 0.2319281 -0.151928076 0.2233959
## 3 Cui FFG RA FFG vs. RA 0.30 0.2927837 0.007216275 0.1889559
## 4 Feng FFG RA FFG vs. RA 0.03 0.2927837 -0.262783725 0.1664332
## 5 Hyun FFG RA FFG vs. RA 1.10 0.2927837 0.807216275 0.4535049
## pch col
## 1 1 black
## 2 1 black
## 3 2 black
## 4 2 black
## 5 2 black
##
## [[5]]
## studlab treat1 treat2 comparison TE TE.direct TE.adj seTE
## 1 Chen FFG CT_Nav FFG vs. CT_Nav 0.12 -0.004269283 0.12426928 0.2295700
## 2 Wang FFG CT_Nav FFG vs. CT_Nav -0.12 -0.004269283 -0.11573072 0.2215428
## 3 Feng FFG RA FFG vs. RA -0.03 0.003712019 -0.03371202 0.1811353
## 4 Hyun FFG RA FFG vs. RA 0.60 0.003712019 0.59628798 0.7617961
## pch col
## 1 1 black
## 2 1 black
## 3 2 black
## 4 2 black
##
## [[6]]
## studlab treat1 treat2 comparison TE TE.direct TE.adj seTE pch
## 1 Chen FFG CT_Nav FFG vs. CT_Nav 176.91 95.9597 80.95030 11.464272 1
## 2 Tian FFG CT_Nav FFG vs. CT_Nav 89.02 95.9597 -6.93970 23.707297 1
## 3 Wang FFG CT_Nav FFG vs. CT_Nav 15.60 95.9597 -80.35970 88.206027 1
## 4 Wu FFG CT_Nav FFG vs. CT_Nav 24.88 95.9597 -71.07970 50.254081 1
## 5 Cui FFG RA FFG vs. RA 158.50 119.7557 38.74433 6.001739 2
## 6 Feng FFG RA FFG vs. RA 72.50 119.7557 -47.25567 31.006581 2
## col
## 1 black
## 2 black
## 3 black
## 4 black
## 5 black
## 6 black