#setwd("/Users/nielspacheco/Dropbox/Mac/Desktop/labs/Baaj/")
# Creating the dataframe
Endovascular_Y1 <- data.frame(
Author_year = c(
"Qi, 2014", "Tsuruta, 2019", "Murphy, 2013", "Cho, 2013", "Ma, 2018",
"Lee, 2015", "Durnford, 2017", "Kirsch, 2013", "Su, 2013", "Sasamori, 2015",
"Yang, 2022", "Oh, 2021", "Watanabe, 2020", "Takai, 2021", "Koch, 2017",
"Koyalmantham, 2020", "Özkan, 2015", "Ronald, 2020", "Vukić, 2021",
"Gemmete, 2013", "Bretonnier, 2019", "Boonyakarnkul, 2023", "Lee, 2021",
"Gross, 2016"
),
E1 = c(
12, 172, 64, 28, 10, 28, 22, 61, 40, 31, 9, 34, 13, 50, 20, 5, 5, 7, 7, 29, 40, 64, 56, 28
),
n_endovascular_First = c(
3, 66, 8, 0, 0, 8, 10, 14, 16, 9, 2, 10, 1, 10, 6, 5, 2, 1, 0, 7, 12, 13, 19, 10
)
)
# Display the dataframe
print(Endovascular_Y1)
## Author_year E1 n_endovascular_First
## 1 Qi, 2014 12 3
## 2 Tsuruta, 2019 172 66
## 3 Murphy, 2013 64 8
## 4 Cho, 2013 28 0
## 5 Ma, 2018 10 0
## 6 Lee, 2015 28 8
## 7 Durnford, 2017 22 10
## 8 Kirsch, 2013 61 14
## 9 Su, 2013 40 16
## 10 Sasamori, 2015 31 9
## 11 Yang, 2022 9 2
## 12 Oh, 2021 34 10
## 13 Watanabe, 2020 13 1
## 14 Takai, 2021 50 10
## 15 Koch, 2017 20 6
## 16 Koyalmantham, 2020 5 5
## 17 Özkan, 2015 5 2
## 18 Ronald, 2020 7 1
## 19 Vukić, 2021 7 0
## 20 Gemmete, 2013 29 7
## 21 Bretonnier, 2019 40 12
## 22 Boonyakarnkul, 2023 64 13
## 23 Lee, 2021 56 19
## 24 Gross, 2016 28 10
# Creating the dataframe
Surgical_Y1 <- data.frame(
Author_year = c(
"Qi, 2014", "Murphy, 2013", "Cho, 2013", "Ma, 2018", "Lee, 2015",
"Durnford, 2017", "Kirsch, 2013", "Zhang, 2022", "Sasamori, 2015",
"Yang, 2022", "Zhang, 2020", "Oh, 2021", "Watanabe, 2020",
"Takai, 2021", "Koch, 2017", "Koyalmantham, 2020", "Luo, 2021",
"O'Reilly, 2022", "Özkan, 2015", "Ronald, 2020", "Vukić, 2021",
"Gemmete, 2013", "Bretonnier, 2019", "Boonyakarnkul, 2023",
"Chibbaro, 2015", "Safaee, 2018", "Lee, 2021", "Gross, 2016"
),
S1 = c(
40, 26, 16, 81, 5, 37, 31, 32, 19, 75, 65, 4, 17, 145, 14, 28, 76, 59,
25, 40, 15, 4, 23, 3, 30, 41, 15, 43
),
n_surgical_First = c(
0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 3, 0, 0, 0, 4, 0, 0, 2, 0,
0, 0, 0, 0
)
)
# Display the dataframe
print(Surgical_Y1)
## Author_year S1 n_surgical_First
## 1 Qi, 2014 40 0
## 2 Murphy, 2013 26 5
## 3 Cho, 2013 16 0
## 4 Ma, 2018 81 0
## 5 Lee, 2015 5 0
## 6 Durnford, 2017 37 0
## 7 Kirsch, 2013 31 0
## 8 Zhang, 2022 32 0
## 9 Sasamori, 2015 19 0
## 10 Yang, 2022 75 0
## 11 Zhang, 2020 65 0
## 12 Oh, 2021 4 0
## 13 Watanabe, 2020 17 3
## 14 Takai, 2021 145 0
## 15 Koch, 2017 14 0
## 16 Koyalmantham, 2020 28 3
## 17 Luo, 2021 76 0
## 18 O'Reilly, 2022 59 0
## 19 Özkan, 2015 25 0
## 20 Ronald, 2020 40 4
## 21 Vukić, 2021 15 0
## 22 Gemmete, 2013 4 0
## 23 Bretonnier, 2019 23 2
## 24 Boonyakarnkul, 2023 3 0
## 25 Chibbaro, 2015 30 0
## 26 Safaee, 2018 41 0
## 27 Lee, 2021 15 0
## 28 Gross, 2016 43 0
# Creating the dataframe
Sub_Endo_Surg <- data.frame(
Author_year = c(
"Qi, 2014", "Ma, 2018", "Durnford, 2017", "Su, 2013", "Sasamori, 2015",
"Yang, 2022", "Zhang, 2020", "Oh, 2021", "Takai, 2021", "Koch, 2017",
"Koyalmantham, 2020", "Özkan, 2015", "Gemmete, 2013", "Bretonnier, 2019",
"Lee, 2021",
"Qi, 2014", "Ma, 2018", "Durnford, 2017", "Su, 2013", "Sasamori, 2015",
"Yang, 2022", "Zhang, 2020", "Oh, 2021", "Takai, 2021", "Koch, 2017",
"Koyalmantham, 2020", "Özkan, 2015", "Gemmete, 2013", "Bretonnier, 2019",
"Lee, 2021"
),
E2 = c(
5, 1, 10, 13, 9, 1, 3, 8, 8, 6, 1, 2, 7, 6, 17,
5, 1, 10, 13, 9, 1, 3, 8, 8, 6, 1, 2, 7, 6, 17
),
partial_occulsion = c(
0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 1, 1,
0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0
),
subgroup = c(
rep("Endo-endo", 15),
rep("Endo-surg", 15)
)
)
# Display the dataframe
print(Sub_Endo_Surg)
## Author_year E2 partial_occulsion subgroup
## 1 Qi, 2014 5 0 Endo-endo
## 2 Ma, 2018 1 0 Endo-endo
## 3 Durnford, 2017 10 0 Endo-endo
## 4 Su, 2013 13 0 Endo-endo
## 5 Sasamori, 2015 9 0 Endo-endo
## 6 Yang, 2022 1 0 Endo-endo
## 7 Zhang, 2020 3 0 Endo-endo
## 8 Oh, 2021 8 2 Endo-endo
## 9 Takai, 2021 8 0 Endo-endo
## 10 Koch, 2017 6 0 Endo-endo
## 11 Koyalmantham, 2020 1 0 Endo-endo
## 12 Özkan, 2015 2 0 Endo-endo
## 13 Gemmete, 2013 7 0 Endo-endo
## 14 Bretonnier, 2019 6 1 Endo-endo
## 15 Lee, 2021 17 1 Endo-endo
## 16 Qi, 2014 5 0 Endo-surg
## 17 Ma, 2018 1 0 Endo-surg
## 18 Durnford, 2017 10 1 Endo-surg
## 19 Su, 2013 13 0 Endo-surg
## 20 Sasamori, 2015 9 0 Endo-surg
## 21 Yang, 2022 1 0 Endo-surg
## 22 Zhang, 2020 3 0 Endo-surg
## 23 Oh, 2021 8 0 Endo-surg
## 24 Takai, 2021 8 0 Endo-surg
## 25 Koch, 2017 6 0 Endo-surg
## 26 Koyalmantham, 2020 1 0 Endo-surg
## 27 Özkan, 2015 2 0 Endo-surg
## 28 Gemmete, 2013 7 0 Endo-surg
## 29 Bretonnier, 2019 6 0 Endo-surg
## 30 Lee, 2021 17 0 Endo-surg
# Creating the dataframe
Sub_Surg_Surg <- data.frame(
Author_year = c(
"Zhang, 2020", "Takai, 2021", "Koyalmantham, 2020", "Ronald, 2020", "Bretonnier, 2019",
"Zhang, 2020", "Takai, 2021", "Koyalmantham, 2020", "Ronald, 2020", "Bretonnier, 2019"
),
S2 = c(
3, 8, 3, 1, 3,
3, 8, 3, 1, 3
),
partial_occlusion = c(
0, 0, 0, 0, 0,
0, 0, 0, 0, 1
),
subgroup = c(
rep("Surg-surg", 5),
rep("Surg-endo", 5)
)
)
# Display the dataframe
print(Sub_Surg_Surg)
## Author_year S2 partial_occlusion subgroup
## 1 Zhang, 2020 3 0 Surg-surg
## 2 Takai, 2021 8 0 Surg-surg
## 3 Koyalmantham, 2020 3 0 Surg-surg
## 4 Ronald, 2020 1 0 Surg-surg
## 5 Bretonnier, 2019 3 0 Surg-surg
## 6 Zhang, 2020 3 0 Surg-endo
## 7 Takai, 2021 8 0 Surg-endo
## 8 Koyalmantham, 2020 3 0 Surg-endo
## 9 Ronald, 2020 1 0 Surg-endo
## 10 Bretonnier, 2019 3 1 Surg-endo
# Load the meta package
library(meta)
## Loading required package: metadat
## Loading 'meta' package (version 7.0-0).
## Type 'help(meta)' for a brief overview.
## Readers of 'Meta-Analysis with R (Use R!)' should install
## older version of 'meta' package: https://tinyurl.com/dt4y5drs
# Perform the meta-analysis for the Endovascular_Y1 dataframe
meta_analysis_Endovascular_Y1 <- metaprop(
event = n_endovascular_First,
n = E1,
data = Endovascular_Y1,
sm = "PLO",
method.tau = "DL",
prediction = FALSE,
comb.fixed = FALSE,
comb.random = TRUE,
studlab = Author_year
)
# Summary of the meta-analysis
summary(meta_analysis_Endovascular_Y1)
## proportion 95%-CI %W(random)
## Qi, 2014 0.2500 [0.0549; 0.5719] 2.7
## Tsuruta, 2019 0.3837 [0.3107; 0.4608] 9.3
## Murphy, 2013 0.1250 [0.0555; 0.2315] 5.5
## Cho, 2013 0.0000 [0.0000; 0.1234] 0.7
## Ma, 2018 0.0000 [0.0000; 0.3085] 0.7
## Lee, 2015 0.2857 [0.1322; 0.4867] 5.0
## Durnford, 2017 0.4545 [0.2439; 0.6779] 4.9
## Kirsch, 2013 0.2295 [0.1315; 0.3550] 6.7
## Su, 2013 0.4000 [0.2486; 0.5667] 6.4
## Sasamori, 2015 0.2903 [0.1422; 0.4804] 5.3
## Yang, 2022 0.2222 [0.0281; 0.6001] 2.0
## Oh, 2021 0.2941 [0.1510; 0.4748] 5.5
## Watanabe, 2020 0.0769 [0.0019; 0.3603] 1.3
## Takai, 2021 0.2000 [0.1003; 0.3372] 5.9
## Koch, 2017 0.3000 [0.1189; 0.5428] 4.2
## Koyalmantham, 2020 1.0000 [0.4782; 1.0000] 0.7
## Özkan, 2015 0.4000 [0.0527; 0.8534] 1.6
## Ronald, 2020 0.1429 [0.0036; 0.5787] 1.2
## Vukić, 2021 0.0000 [0.0000; 0.4096] 0.7
## Gemmete, 2013 0.2414 [0.1030; 0.4354] 4.8
## Bretonnier, 2019 0.3000 [0.1656; 0.4653] 6.0
## Boonyakarnkul, 2023 0.2031 [0.1128; 0.3223] 6.6
## Lee, 2021 0.3393 [0.2181; 0.4781] 7.0
## Gross, 2016 0.3571 [0.1864; 0.5593] 5.3
##
## Number of studies: k = 24
## Number of observations: o = 835
## Number of events: e = 232
##
## proportion 95%-CI
## Random effects model 0.2751 [0.2283; 0.3274]
##
## Quantifying heterogeneity:
## tau^2 = 0.1493 [0.0513; 0.9457]; tau = 0.3864 [0.2266; 0.9725]
## I^2 = 47.7% [15.8%; 67.5%]; H = 1.38 [1.09; 1.75]
##
## Test of heterogeneity:
## Q d.f. p-value
## 43.94 23 0.0053
##
## Details on meta-analytical method:
## - Inverse variance method
## - DerSimonian-Laird estimator for tau^2
## - Jackson method for confidence interval of tau^2 and tau
## - Logit transformation
## - Clopper-Pearson confidence interval for individual studies
## - Continuity correction of 0.5 in studies with zero cell frequencies
# To visualize the results, you can plot a forest plot
meta::forest(meta_analysis_Endovascular_Y1, layout = "RevMan")
# Load the meta package
library(meta)
# Perform the meta-analysis for the Endovascular_Y1 dataframe
meta_analysis_Surgical_Y1 <- metaprop(
event = n_surgical_First,
n = S1,
data = Surgical_Y1,
sm = "PLO",
method.tau = "DL",
prediction = FALSE,
comb.fixed = FALSE,
comb.random = TRUE,
studlab = Author_year
)
# Summary of the meta-analysis
summary(meta_analysis_Endovascular_Y1)
## proportion 95%-CI %W(random)
## Qi, 2014 0.2500 [0.0549; 0.5719] 2.7
## Tsuruta, 2019 0.3837 [0.3107; 0.4608] 9.3
## Murphy, 2013 0.1250 [0.0555; 0.2315] 5.5
## Cho, 2013 0.0000 [0.0000; 0.1234] 0.7
## Ma, 2018 0.0000 [0.0000; 0.3085] 0.7
## Lee, 2015 0.2857 [0.1322; 0.4867] 5.0
## Durnford, 2017 0.4545 [0.2439; 0.6779] 4.9
## Kirsch, 2013 0.2295 [0.1315; 0.3550] 6.7
## Su, 2013 0.4000 [0.2486; 0.5667] 6.4
## Sasamori, 2015 0.2903 [0.1422; 0.4804] 5.3
## Yang, 2022 0.2222 [0.0281; 0.6001] 2.0
## Oh, 2021 0.2941 [0.1510; 0.4748] 5.5
## Watanabe, 2020 0.0769 [0.0019; 0.3603] 1.3
## Takai, 2021 0.2000 [0.1003; 0.3372] 5.9
## Koch, 2017 0.3000 [0.1189; 0.5428] 4.2
## Koyalmantham, 2020 1.0000 [0.4782; 1.0000] 0.7
## Özkan, 2015 0.4000 [0.0527; 0.8534] 1.6
## Ronald, 2020 0.1429 [0.0036; 0.5787] 1.2
## Vukić, 2021 0.0000 [0.0000; 0.4096] 0.7
## Gemmete, 2013 0.2414 [0.1030; 0.4354] 4.8
## Bretonnier, 2019 0.3000 [0.1656; 0.4653] 6.0
## Boonyakarnkul, 2023 0.2031 [0.1128; 0.3223] 6.6
## Lee, 2021 0.3393 [0.2181; 0.4781] 7.0
## Gross, 2016 0.3571 [0.1864; 0.5593] 5.3
##
## Number of studies: k = 24
## Number of observations: o = 835
## Number of events: e = 232
##
## proportion 95%-CI
## Random effects model 0.2751 [0.2283; 0.3274]
##
## Quantifying heterogeneity:
## tau^2 = 0.1493 [0.0513; 0.9457]; tau = 0.3864 [0.2266; 0.9725]
## I^2 = 47.7% [15.8%; 67.5%]; H = 1.38 [1.09; 1.75]
##
## Test of heterogeneity:
## Q d.f. p-value
## 43.94 23 0.0053
##
## Details on meta-analytical method:
## - Inverse variance method
## - DerSimonian-Laird estimator for tau^2
## - Jackson method for confidence interval of tau^2 and tau
## - Logit transformation
## - Clopper-Pearson confidence interval for individual studies
## - Continuity correction of 0.5 in studies with zero cell frequencies
# To visualize the results, you can plot a forest plot
meta::forest(meta_analysis_Surgical_Y1, layout = "RevMan")
# Load the meta package
library(meta)
# Perform the meta-analysis for the Endovascular_Y1 dataframe
meta_analysis_Sub_Endo_Surg <- metaprop(
event = partial_occulsion,
n = E2,
data = Sub_Endo_Surg,
sm = "PLO",
method.tau = "DL",
prediction = FALSE,
comb.fixed = FALSE,
comb.random = TRUE,
studlab = Author_year,
byvar = subgroup
)
# Summary of the meta-analysis
summary(meta_analysis_Sub_Endo_Surg)
## proportion 95%-CI %W(random) subgroup
## Qi, 2014 0.0000 [0.0000; 0.5218] 2.9 Endo-endo
## Ma, 2018 0.0000 [0.0000; 0.9750] 2.4 Endo-endo
## Durnford, 2017 0.0000 [0.0000; 0.3085] 3.0 Endo-endo
## Su, 2013 0.0000 [0.0000; 0.2471] 3.1 Endo-endo
## Sasamori, 2015 0.0000 [0.0000; 0.3363] 3.0 Endo-endo
## Yang, 2022 0.0000 [0.0000; 0.9750] 2.4 Endo-endo
## Zhang, 2020 0.0000 [0.0000; 0.7076] 2.8 Endo-endo
## Oh, 2021 0.2500 [0.0319; 0.6509] 9.6 Endo-endo
## Takai, 2021 0.0000 [0.0000; 0.3694] 3.0 Endo-endo
## Koch, 2017 0.0000 [0.0000; 0.4593] 3.0 Endo-endo
## Koyalmantham, 2020 0.0000 [0.0000; 0.9750] 2.4 Endo-endo
## Özkan, 2015 0.0000 [0.0000; 0.8419] 2.7 Endo-endo
## Gemmete, 2013 0.0000 [0.0000; 0.4096] 3.0 Endo-endo
## Bretonnier, 2019 0.1667 [0.0042; 0.6412] 5.3 Endo-endo
## Lee, 2021 0.0588 [0.0015; 0.2869] 6.0 Endo-endo
## Qi, 2014 0.0000 [0.0000; 0.5218] 2.9 Endo-surg
## Ma, 2018 0.0000 [0.0000; 0.9750] 2.4 Endo-surg
## Durnford, 2017 0.1000 [0.0025; 0.4450] 5.7 Endo-surg
## Su, 2013 0.0000 [0.0000; 0.2471] 3.1 Endo-surg
## Sasamori, 2015 0.0000 [0.0000; 0.3363] 3.0 Endo-surg
## Yang, 2022 0.0000 [0.0000; 0.9750] 2.4 Endo-surg
## Zhang, 2020 0.0000 [0.0000; 0.7076] 2.8 Endo-surg
## Oh, 2021 0.0000 [0.0000; 0.3694] 3.0 Endo-surg
## Takai, 2021 0.0000 [0.0000; 0.3694] 3.0 Endo-surg
## Koch, 2017 0.0000 [0.0000; 0.4593] 3.0 Endo-surg
## Koyalmantham, 2020 0.0000 [0.0000; 0.9750] 2.4 Endo-surg
## Özkan, 2015 0.0000 [0.0000; 0.8419] 2.7 Endo-surg
## Gemmete, 2013 0.0000 [0.0000; 0.4096] 3.0 Endo-surg
## Bretonnier, 2019 0.0000 [0.0000; 0.4593] 3.0 Endo-surg
## Lee, 2021 0.0000 [0.0000; 0.1951] 3.1 Endo-surg
##
## Number of studies: k = 30
## Number of observations: o = 194
## Number of events: e = 5
##
## proportion 95%-CI
## Random effects model 0.0992 [0.0629; 0.1531]
##
## Quantifying heterogeneity:
## tau^2 = 0; tau = 0; I^2 = 0.0% [0.0%; 40.8%]; H = 1.00 [1.00; 1.30]
##
## Test of heterogeneity:
## Q d.f. p-value
## 9.44 29 0.9998
##
## Results for subgroups (random effects model):
## k proportion 95%-CI tau^2 tau Q I^2
## subgroup = Endo-endo 15 0.1121 [0.0607; 0.1980] 0 0 5.38 0.0%
## subgroup = Endo-surg 15 0.0855 [0.0429; 0.1631] 0 0 3.71 0.0%
##
## Test for subgroup differences (random effects model):
## Q d.f. p-value
## Between groups 0.35 1 0.5537
##
## Details on meta-analytical method:
## - Inverse variance method
## - DerSimonian-Laird estimator for tau^2
## - Logit transformation
## - Clopper-Pearson confidence interval for individual studies
## - Continuity correction of 0.5 in studies with zero cell frequencies
# To visualize the results, you can plot a forest plot
meta::forest(meta_analysis_Sub_Endo_Surg, layout = "RevMan")
# Load the meta package
library(meta)
# Perform the meta-analysis for the Endovascular_Y1 dataframe
meta_analysis_Sub_Surg_Surg <- metaprop(
event = partial_occlusion,
n = S2,
data = Sub_Surg_Surg,
sm = "PLO",
method.tau = "DL",
prediction = FALSE,
comb.fixed = FALSE,
comb.random = TRUE,
studlab = Author_year,
byvar = subgroup
)
# Summary of the meta-analysis
summary(meta_analysis_Sub_Surg_Surg)
## proportion 95%-CI %W(random) subgroup
## Zhang, 2020 0.0000 [0.0000; 0.7076] 9.6 Surg-surg
## Takai, 2021 0.0000 [0.0000; 0.3694] 10.4 Surg-surg
## Koyalmantham, 2020 0.0000 [0.0000; 0.7076] 9.6 Surg-surg
## Ronald, 2020 0.0000 [0.0000; 0.9750] 8.2 Surg-surg
## Bretonnier, 2019 0.0000 [0.0000; 0.7076] 9.6 Surg-surg
## Zhang, 2020 0.0000 [0.0000; 0.7076] 9.6 Surg-endo
## Takai, 2021 0.0000 [0.0000; 0.3694] 10.4 Surg-endo
## Koyalmantham, 2020 0.0000 [0.0000; 0.7076] 9.6 Surg-endo
## Ronald, 2020 0.0000 [0.0000; 0.9750] 8.2 Surg-endo
## Bretonnier, 2019 0.3333 [0.0084; 0.9057] 14.7 Surg-endo
##
## Number of studies: k = 10
## Number of observations: o = 36
## Number of events: e = 1
##
## proportion 95%-CI
## Random effects model 0.1410 [0.0615; 0.2916]
##
## Quantifying heterogeneity:
## tau^2 = 0; tau = 0; I^2 = 0.0% [0.0%; 62.4%]; H = 1.00 [1.00; 1.63]
##
## Test of heterogeneity:
## Q d.f. p-value
## 2.24 9 0.9871
##
## Results for subgroups (random effects model):
## k proportion 95%-CI tau^2 tau Q I^2
## subgroup = Surg-surg 5 0.1200 [0.0347; 0.3409] 0 0 0.64 0.0%
## subgroup = Surg-endo 5 0.1626 [0.0518; 0.4084] 0 0 1.46 0.0%
##
## Test for subgroup differences (random effects model):
## Q d.f. p-value
## Between groups 0.14 1 0.7061
##
## Details on meta-analytical method:
## - Inverse variance method
## - DerSimonian-Laird estimator for tau^2
## - Logit transformation
## - Clopper-Pearson confidence interval for individual studies
## - Continuity correction of 0.5 in studies with zero cell frequencies
# To visualize the results, you can plot a forest plot
meta::forest(meta_analysis_Sub_Surg_Surg, layout = "RevMan")
