set up
mlabfun <- function(text, x) {
list(bquote(paste(.(text),
" (Q = ", .(fmtx(x$QE, digits=2)),
", df = ", .(x$k - x$p), ", ",
.(fmtp(x$QEp, digits=3, pname="p", add0=TRUE, sep=TRUE, equal=TRUE)), "; ",
I^2, " = ", .(fmtx(x$I2, digits=1)), "%, ",
tau^2, " = ", .(fmtx(x$tau2, digits=2)), ")")))}
library(tidyverse)
library(meta)
library(metafor)
library(dmetar)
library(ggplot2)
library(robvis)
library(readxl)
library(broom)
library(stargazer)
MEANS
IKDC Subjective
## Review: ikdc_subjective
##
## mean 95%-CI %W(random) graft_type
## Vilchez-Cavazos et al., 2020 88.0000 [84.3332; 91.6668] 2.4 B-QT
## Sinding et al., 2020 76.0000 [70.8587; 81.1413] 2.2 B-QT
## Setliff et al., 2023 80.9000 [68.2562; 93.5438] 1.3 B-QT
## Runer et al., 2023 93.9000 [91.3813; 96.4187] 2.5 B-QT
## Pomenta Bastidas et al., 2022 86.2800 [83.2998; 89.2602] 2.4 B-QT
## Lund et al., 2014 84.0000 [78.4399; 89.5601] 2.2 B-QT
## Lind et al., 2020b 82.0000 [77.9975; 86.0025] 2.3 B-QT
## Lee et al., 2021 81.0000 [79.6036; 82.3964] 2.5 B-QT
## Lee et al., 2016 80.2000 [77.3710; 83.0290] 2.5 B-QT
## Lee et al., 2014 77.2500 [74.6574; 79.8426] 2.5 B-QT
## Kwak et al., 2018 67.3000 [62.3915; 72.2085] 2.2 B-QT
## Komzák et al., 2022 78.3000 [75.0461; 81.5539] 2.4 B-QT
## Kim et al., 2018 71.0700 [68.9087; 73.2313] 2.5 B-QT
## Kim et al., 2014 87.0200 [85.8950; 88.1450] 2.6 B-QT
## Irrgang et al., 2021 92.1400 [90.9160; 93.3640] 2.5 B-QT
## Horstmann et al., 2022 89.3000 [84.4191; 94.1809] 2.2 B-QT
## Hofer et al., 2022 88.0000 [83.3228; 92.6772] 2.3 B-QT
## Herman et al., 2023 91.6000 [86.5189; 96.6811] 2.2 B-QT
## Guney-Deniz et al., 2020 82.7000 [78.8974; 86.5026] 2.4 B-QT
## Fu et al., 2019 89.8000 [86.8881; 92.7119] 2.4 B-QT
## Cavaignac et al., 2017 84.0000 [80.1588; 87.8412] 2.4 B-QT
## Barié et al., 2020 92.0000 [87.0815; 96.9185] 2.2 B-QT
## Barié et al., 2018 90.0000 [87.4299; 92.5701] 2.5 B-QT
## Barié et al., 2010 83.7800 [81.2443; 86.3157] 2.5 B-QT
## Akoto et al., 2019 86.4000 [82.0535; 90.7465] 2.3 B-QT
## Akoto et al., 2015 89.5000 [84.3557; 94.6443] 2.2 B-QT
## Akoto and Hoeher, 2012 86.1000 [80.4461; 91.7539] 2.2 B-QT
## Tang et al., 2023 87.1200 [83.9779; 90.2621] 2.4 S-QT
## Setliff et al., 2023 81.0000 [77.6184; 84.3816] 2.4 S-QT
## Schulz et al., 2013 80.4400 [77.2554; 83.6246] 2.4 S-QT
## Renfree et al., 2023 90.5000 [88.2133; 92.7867] 2.5 S-QT
## Pichler et al., 2022 93.9000 [89.5893; 98.2107] 2.3 S-QT
## Perez et al., 2019 94.8300 [92.6298; 97.0302] 2.5 S-QT
## Letter et al., 2023 69.4200 [64.9881; 73.8519] 2.3 S-QT
## Letter et al., 2019 71.7100 [65.8108; 77.6092] 2.1 S-QT
## Johnston et al., 2022 88.5000 [84.3769; 92.6231] 2.3 S-QT
## Johnston et al., 2021 77.7100 [73.0290; 82.3910] 2.3 S-QT
## Hunnicutt et al., 2019 81.2900 [77.3326; 85.2474] 2.3 S-QT
## Hogan et al., 2022 77.4000 [66.3387; 88.4613] 1.5 S-QT
## Greif et al., 2022 91.1000 [89.1375; 93.0625] 2.5 S-QT
## Gille et al., 2009 78.3000 [74.3979; 82.2021] 2.4 S-QT
## DeAngelis et al., 2007 84.7000 [81.9454; 87.4546] 2.5 S-QT
## Brinkman et al., 2023 90.7000 [88.7667; 92.6333] 2.5 S-QT
##
## Number of studies: k = 43
## Number of observations: o = 2049
##
## mean 95%-CI
## Random effects model 84.2919 [82.1769; 86.4069]
##
## Quantifying heterogeneity:
## tau^2 = 42.9409 [27.7064; 71.7283]; tau = 6.5529 [5.2637; 8.4693]
## I^2 = 95.0% [94.0%; 95.8%]; H = 4.47 [4.07; 4.90]
##
## Test of heterogeneity:
## Q d.f. p-value
## 837.58 42 < 0.0001
##
## Results for subgroups (random effects model):
## k mean 95%-CI tau^2 tau Q I^2
## graft_type = B-QT 27 84.4461 [81.8690; 87.0231] 38.6429 6.2163 559.32 95.4%
## graft_type = S-QT 16 83.9747 [79.8899; 88.0595] 53.9493 7.3450 260.57 94.2%
##
## Test for subgroup differences (random effects model):
## Q d.f. p-value
## Between groups 0.04 1 0.8369
##
## Details on meta-analytical method:
## - Inverse variance method
## - Restricted maximum-likelihood estimator for tau^2
## - Q-Profile method for confidence interval of tau^2 and tau
## - Hartung-Knapp adjustment for random effects model (df = 42)
## - Untransformed (raw) means
##
## S-QT B-QT Sum
## Short 9 10 19
## Medium 6 12 18
## Long 1 5 6
## Sum 16 27 43
##
## Mixed-Effects Model (k = 43; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## -122.4664 244.9329 258.9329 270.2093 262.7950
##
## tau^2 (estimated amount of residual heterogeneity): 39.4828 (SE = 10.2044)
## tau (square root of estimated tau^2 value): 6.2835
## I^2 (residual heterogeneity / unaccounted variability): 94.47%
## H^2 (unaccounted variability / sampling variability): 18.07
##
## Test for Residual Heterogeneity:
## QE(df = 37) = 639.7615, p-val < .0001
##
## Test of Moderators (coefficients 1:6):
## F(df1 = 6, df2 = 37) = 1175.2201, p-val < .0001
##
## Model Results:
##
## estimate se tval df pval
## follow_up_minimumShort:graft_typeS-QT 80.1345 2.2557 35.5253 37 <.0001
## follow_up_minimumMedium:graft_typeS-QT 88.1748 2.6061 33.8342 37 <.0001
## follow_up_minimumLong:graft_typeS-QT 90.7000 6.3200 14.3514 37 <.0001
## follow_up_minimumShort:graft_typeB-QT 83.2340 2.1365 38.9584 37 <.0001
## follow_up_minimumMedium:graft_typeB-QT 83.8329 1.8797 44.5993 37 <.0001
## follow_up_minimumLong:graft_typeB-QT 88.1011 2.8863 30.5239 37 <.0001
## ci.lb ci.ub
## follow_up_minimumShort:graft_typeS-QT 75.5640 84.7050 ***
## follow_up_minimumMedium:graft_typeS-QT 82.8944 93.4553 ***
## follow_up_minimumLong:graft_typeS-QT 77.8945 103.5055 ***
## follow_up_minimumShort:graft_typeB-QT 78.9050 87.5629 ***
## follow_up_minimumMedium:graft_typeB-QT 80.0243 87.6415 ***
## follow_up_minimumLong:graft_typeB-QT 82.2529 93.9493 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
