# Create the dataframe
Sub_Endo_Surg <- data.frame(
Author_year = c("Qi, 2014", "Ma, 2018", "Durnford, 2017", "Su, 2013", "Sasamori, 2015",
"Yang, 2022", "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, 13, 8, 6, 1, 2, 7, 6, 17,
5, 1, 10, 13, 9, 1, 3, 8, 8, 6, 1, 2, 7, 6, 17),
partial_occlusion = c(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("Endo-endo", "Endo-endo", "Endo-endo", "Endo-endo", "Endo-endo",
"Endo-endo", "Endo-endo", "Endo-endo", "Endo-endo", "Endo-endo",
"Endo-endo", "Endo-endo", "Endo-endo", "Endo-endo",
"Endo-surg", "Endo-surg", "Endo-surg", "Endo-surg", "Endo-surg",
"Endo-surg", "Endo-surg", "Endo-surg", "Endo-surg", "Endo-surg",
"Endo-surg", "Endo-surg", "Endo-surg", "Endo-surg", "Endo-surg")
)
# Print the dataframe
print(Sub_Endo_Surg)
## Author_year E2 partial_occlusion 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 Oh, 2021 13 2 Endo-endo
## 8 Takai, 2021 8 0 Endo-endo
## 9 Koch 2017 6 0 Endo-endo
## 10 Koyalmantham, 2020 1 0 Endo-endo
## 11 Özkan, 2015 2 0 Endo-endo
## 12 Gemmete, 2013 7 0 Endo-endo
## 13 Bretonnier, 2019 6 1 Endo-endo
## 14 Lee, 2021 17 1 Endo-endo
## 15 Qi, 2014 5 0 Endo-surg
## 16 Ma, 2018 1 0 Endo-surg
## 17 Durnford, 2017 10 1 Endo-surg
## 18 Su, 2013 13 0 Endo-surg
## 19 Sasamori, 2015 9 0 Endo-surg
## 20 Yang, 2022 1 0 Endo-surg
## 21 Zhang, 2020 3 0 Endo-surg
## 22 Oh, 2021 8 0 Endo-surg
## 23 Takai, 2021 8 0 Endo-surg
## 24 Koch 2017 6 0 Endo-surg
## 25 Koyalmantham, 2020 1 0 Endo-surg
## 26 Özkan, 2015 2 0 Endo-surg
## 27 Gemmete, 2013 7 0 Endo-surg
## 28 Bretonnier, 2019 6 0 Endo-surg
## 29 Lee, 2021 17 0 Endo-surg
# Create 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, 1, 3, 1, 3,
3, 1, 3, 1, 3),
partial_occlusion = c(0, 0, 0, 0, 0,
0, 0, 0, 0, 1),
subgroup = c("Surg-surg", "Surg-surg", "Surg-surg", "Surg-surg", "Surg-surg",
"Surg-endo", "Surg-endo", "Surg-endo", "Surg-endo", "Surg-endo")
)
# Print the dataframe
print(Sub_Surg_Surg)
## Author_year S2 partial_occlusion subgroup
## 1 Zhang, 2020 3 0 Surg-surg
## 2 Takai, 2021 1 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 1 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 8.0-2).
## Type 'help(meta)' for a brief overview.
# Perform the meta-analysis for the Endovascular_Y1 dataframe
meta_analysis_Sub_Endo_Surg <- metaprop(
event = partial_occlusion,
n = E2,
data = Sub_Endo_Surg,
sm = "PLO",
method.tau = "DL",
prediction = FALSE,
comb.fixed = FALSE,
comb.random = TRUE,
studlab = Author_year,
byvar = subgroup
)
## Warning: Use argument 'common' instead of 'comb.fixed' (deprecated).
## Warning: Use argument 'random' instead of 'comb.random' (deprecated).
## Warning: Use argument 'subgroup' instead of 'byvar' (deprecated).
# 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] 3.0 Endo-endo
## Ma, 2018 0.0000 [0.0000; 0.9750] 2.4 Endo-endo
## Durnford, 2017 0.0000 [0.0000; 0.3085] 3.1 Endo-endo
## Su, 2013 0.0000 [0.0000; 0.2471] 3.1 Endo-endo
## Sasamori, 2015 0.0000 [0.0000; 0.3363] 3.1 Endo-endo
## Yang, 2022 0.0000 [0.0000; 0.9750] 2.4 Endo-endo
## Oh, 2021 0.1538 [0.0192; 0.4545] 11.0 Endo-endo
## Takai, 2021 0.0000 [0.0000; 0.3694] 3.1 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] 5.4 Endo-endo
## Lee, 2021 0.0588 [0.0015; 0.2869] 6.1 Endo-endo
## Qi, 2014 0.0000 [0.0000; 0.5218] 3.0 Endo-surg
## Ma, 2018 0.0000 [0.0000; 0.9750] 2.4 Endo-surg
## Durnford, 2017 0.1000 [0.0025; 0.4450] 5.8 Endo-surg
## Su, 2013 0.0000 [0.0000; 0.2471] 3.1 Endo-surg
## Sasamori, 2015 0.0000 [0.0000; 0.3363] 3.1 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] 3.1 Endo-surg
## Takai, 2021 0.0000 [0.0000; 0.3694] 3.1 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] 3.2 Endo-surg
##
## Number of studies: k = 29
## Number of observations: o = 196
## Number of events: e = 5
##
## proportion 95%-CI
## Random effects model 0.0940 [0.0593; 0.1460]
##
## Quantifying heterogeneity (with 95%-CIs):
## tau^2 = 0; tau = 0; I^2 = 0.0% [0.0%; 41.3%]; H = 1.00 [1.00; 1.31]
##
## Test of heterogeneity:
## Q d.f. p-value
## 7.95 28 0.9999
##
## Results for subgroups (random effects model):
## k proportion 95%-CI tau^2 tau Q I^2
## subgroup = Endo-endo 14 0.1019 [0.0543; 0.1829] 0 0 4.09 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 0.14 1 0.7056
##
## Details of meta-analysis methods:
## - Inverse variance method
## - DerSimonian-Laird estimator for tau^2
## - Calculation of I^2 based on Q
## - 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
)
## Warning: Use argument 'common' instead of 'comb.fixed' (deprecated).
## Warning: Use argument 'random' instead of 'comb.random' (deprecated).
## Warning: Use argument 'subgroup' instead of 'byvar' (deprecated).
# 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] 10.0 Surg-surg
## Takai, 2021 0.0000 [0.0000; 0.9750] 8.6 Surg-surg
## Koyalmantham, 2020 0.0000 [0.0000; 0.7076] 10.0 Surg-surg
## Ronald, 2020 0.0000 [0.0000; 0.9750] 8.6 Surg-surg
## Bretonnier, 2019 0.0000 [0.0000; 0.7076] 10.0 Surg-surg
## Zhang, 2020 0.0000 [0.0000; 0.7076] 10.0 Surg-endo
## Takai, 2021 0.0000 [0.0000; 0.9750] 8.6 Surg-endo
## Koyalmantham, 2020 0.0000 [0.0000; 0.7076] 10.0 Surg-endo
## Ronald, 2020 0.0000 [0.0000; 0.9750] 8.6 Surg-endo
## Bretonnier, 2019 0.3333 [0.0084; 0.9057] 15.3 Surg-endo
##
## Number of studies: k = 10
## Number of observations: o = 22
## Number of events: e = 1
##
## proportion 95%-CI
## Random effects model 0.1881 [0.0831; 0.3722]
##
## Quantifying heterogeneity (with 95%-CIs):
## 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
## 1.10 9 0.9992
##
## Results for subgroups (random effects model):
## k proportion 95%-CI tau^2 tau Q I^2
## subgroup = Surg-surg 5 0.1628 [0.0473; 0.4322] 0 0 0.34 0.0%
## subgroup = Surg-endo 5 0.2135 [0.0692; 0.4976] 0 0 0.64 0.0%
##
## Test for subgroup differences (random effects model):
## Q d.f. p-value
## Between groups 0.12 1 0.7281
##
## Details of meta-analysis methods:
## - Inverse variance method
## - DerSimonian-Laird estimator for tau^2
## - Calculation of I^2 based on Q
## - 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")
