Supplementary Materials for ‘Social Functioning in Children and Adolescents with ADHD: A Meta-analysis’
Haza B, Gosling CJ, Conty L, & Pinabiaux C (2022)
24/10/2022
Supplement 3. Excluded studies
Supplement 4. Measures
Supplement 5. Included studies
## New names:
## * `` -> `...15`
## * `` -> `...17`
## * `` -> `...18`
## * `` -> `...20`Supplement 6. Systematic review
Effect size calculations
Overall N studies + participants
n_sample = dat.meta.long %>%
summarise(n=length(unique(paste(ID_study, indep_sample))))
data.frame(n_sample=n_sample, dat_tot)## n n_studies n_es n_nexp_out n_exp_out
## 1 152 142 652 7983 8300N studies (adjustment discarded)
dat.borenstein %>%
group_by(outcome_SOC_agg) %>%
summarise(n_es = sum(trial),
n_exp = sum(n_exp),
n_nexp = sum(n_nexp),
n_study2 = length(unique(ID_study)))## # A tibble: 5 x 5
## outcome_SOC_agg n_es n_exp n_nexp n_study2
## <chr> <dbl> <dbl> <dbl> <int>
## 1 Empathy 31 507 560 13
## 2 Everyday social skills 225 6177 5719 87
## 3 Facial emotion recognition 188 2154 2228 45
## 4 Non-facial emotion recognition 28 522 580 9
## 5 Theory of mind 180 2542 2455 52N studies (adjustment splitted)
synth_n = dat.borenstein %>%
group_by(outcome_SOC) %>%
summarise(n_es = sum(trial),
n_exp = sum(n_exp),
n_nexp = sum(n_nexp),
nstudy1 = n(),
n_study2 = length(unique(ID_study)))
synth_n## # A tibble: 10 x 6
## outcome_SOC n_es n_exp n_nexp nstudy1 n_study2
## <chr> <dbl> <dbl> <dbl> <int> <int>
## 1 Empathy (adjusted) 6 71 80 3 3
## 2 Empathy (non-adjusted) 25 436 480 12 12
## 3 Everyday social skills (adjusted) 16 607 519 9 9
## 4 Everyday social skills (non-adjusted) 209 5570 5200 82 82
## 5 Facial emotion recognition (adjusted) 20 442 571 7 7
## 6 Facial emotion recognition (non-adjuste~ 168 1712 1657 42 42
## 7 Non-facial emotion recognition (adjuste~ 3 141 254 2 2
## 8 Non-facial emotion recognition (non-adj~ 25 381 326 8 8
## 9 Theory of mind (adjusted) 19 258 290 8 8
## 10 Theory of mind (non-adjusted) 161 2284 2165 49 49dat.meta.long %>%
group_by(outcome_SOC) %>%
summarise(n=length(unique(paste(ID_study, indep_sample))))## # A tibble: 10 x 2
## outcome_SOC n
## <chr> <int>
## 1 Empathy (adjusted) 3
## 2 Empathy (non-adjusted) 14
## 3 Everyday social skills (adjusted) 9
## 4 Everyday social skills (non-adjusted) 86
## 5 Facial emotion recognition (adjusted) 8
## 6 Facial emotion recognition (non-adjusted) 44
## 7 Non-facial emotion recognition (adjusted) 2
## 8 Non-facial emotion recognition (non-adjusted) 8
## 9 Theory of mind (adjusted) 8
## 10 Theory of mind (non-adjusted) 52Supplement 7. ES visualization
Distribution of ES
ggplot(dat.meta, aes(x = es_adj, y = es)) +
geom_jitter(alpha = 0.3, width = 0.35, size = 1/dat.meta$se) +
geom_violin(size = 0.5, alpha = 0.5) +
theme_bw() 
Distribution of ES / outcomes
2 plots, tu choisis celui qui tu préfères
ggplot(dat.meta, aes(x = outcome_SOC_agg, y = es)) +
geom_jitter(width = 0.1, alpha = 0.2, aes(fill = ID, size = 1/se)) + # geom_boxplot(trim=FALSE, alpha = 0.6) +
facet_grid(es_adj ~ "") +
theme_bw() +
labs(y = "Standardized mean difference (SMD)", x = "") +
guides(fill=FALSE) +
coord_flip()
ggplot(dat.meta, aes(x = outcome_SOC_agg, y = es)) +
geom_violin(aes(fill = outcome_SOC_agg), alpha = 0.3) +
geom_jitter(width = 0.1, alpha = 0.2, aes(fill = ID, size = 1/se)) +
geom_boxplot(width=0.1)+
facet_grid(es_adj ~ "") +
theme_bw() +
labs(y = "Standardized mean difference (SMD)", x = "") +
theme(text = element_text(size = 18),
legend.position = "none",
axis.title.y = element_text(size=12, face="bold"),
axis.title.x = element_text(size=12, face="bold")) +
coord_flip() 
Supplement 8. Main analysis
Data analysis
dat.meta$outcome_MOD = ifelse(
dat.meta$outcome_SOC_agg == "Everyday social skills",
"Everyday social skills", "Social cognition")
dat.crude = subset(dat.meta, es_adj == " Non-Adjusted")
dat.adjusted = subset(dat.meta, es_adj == " Adjusted")
V.SCE <- with(dat.crude,
clubSandwich::impute_covariance_matrix(
vi = se^2,
cluster = ID_study,
r = 0.8,
return_list = FALSE,
smooth_vi = TRUE,
subgroup = outcome_SOC))
# all.equal(dat.meta$se^2, diag(unlist(V.SCE)))
SCE <- metafor::rma.mv(yi = es, V = V.SCE,
data = dat.crude,
mods = ~ outcome_SOC - 1,
random = ~ outcome_SOC | ID_study,
struct = "DIAG",
sparse = TRUE)
SCE.ISQ <- metafor::rma.mv(yi = es, V = V.SCE,
data = dat.crude,
mods = ~ outcome_SOC - 1)
het = function(x, y) {
i2 = NA
for (i in 1:nrow(vcov(x))) {
i2[i] = 100 * (vcov(x)[i,i] - vcov(y)[i,i]) / vcov(x)[i,i]
}
return(i2)
}
res.SCE <- data.frame(clubSandwich::coef_test(SCE, vcov = "CR2", cluster = dat.crude$ID_study)) # , cluster = df.ES$researchgroup
res.SCE.ci = data.frame(clubSandwich::conf_int(SCE, vcov = "CR2", cluster = dat.crude$ID_study))
res.SCE$target <- gsub("outcome_SOC", "", row.names(res.SCE))
res.SCE.ci$target <- gsub("outcome_SOC", "", row.names(res.SCE.ci))
res.SCE = dplyr::left_join(res.SCE, res.SCE.ci)
res.SCE$tau2 = SCE$tau2
res.SCE$I2 = het(SCE, SCE.ISQ)library(clubSandwich)
# Empathy v ToM
Wald_test(SCE,
constraints = matrix(c(1,0,0,0,-1), nrow=1),
vcov = "CR2", cluster = dat.crude$ID_study)## test Fstat df_num df_denom p_val sig
## HTZ 2.15 1 13.7 0.165# facial v non facial
Wald_test(SCE,
constraints = matrix(c(0,0,1,-1, 0), nrow=1),
vcov = "CR2", cluster = dat.crude$ID_study)## test Fstat df_num df_denom p_val sig
## HTZ 7.13 1 12.6 0.0197 *# soc-cog v everyday
Wald_test(SCE,
constraints = matrix(c(1,-1,1,1,1), nrow=1),
vcov = "CR2", cluster = dat.crude$ID_study)## test Fstat df_num df_denom p_val sig
## HTZ 11.4 1 26.4 0.00232 **Wald_test(SCE,
constraints = matrix(c(1,-1,0,0,0), nrow=1),
vcov = "CR2", cluster = dat.crude$ID_study)## test Fstat df_num df_denom p_val sig
## HTZ 8.7 1 13.6 0.0108 *Wald_test(SCE,
constraints = matrix(c(0,-1,1,0,0), nrow=1),
vcov = "CR2", cluster = dat.crude$ID_study)## test Fstat df_num df_denom p_val sig
## HTZ 29 1 81.7 <0.001 ***Wald_test(SCE,
constraints = matrix(c(0,-1,0,1,0), nrow=1),
vcov = "CR2", cluster = dat.crude$ID_study)## test Fstat df_num df_denom p_val sig
## HTZ 41.7 1 12 <0.001 ***Wald_test(SCE,
constraints = matrix(c(0,-1,0,0,1), nrow=1),
vcov = "CR2", cluster = dat.crude$ID_study)## test Fstat df_num df_denom p_val sig
## HTZ 14 1 93.5 <0.001 ***res_main <- left_join(res.SCE, dat_SOC)## Joining, by = "target"res_main$p_Satt_bonferroni = res_main$p_Satt * 8
res_main$raw_outcome <- gsub(r"{\s*\([^\)]+\)}","",
as.character(res_main$outcome_SOC))
res_main$outcome_SOC <- gsub("social", "Social", res_main$outcome_SOC)
res_main$outcome_SOC <- gsub("skill", "Skill", res_main$outcome_SOC)
res_main$outcome_SOC <- gsub("emotion", "Emotion", res_main$outcome_SOC)
res_main$outcome_SOC <- gsub("recognition", "Recognition", res_main$outcome_SOC)
res_main$outcome_SOC <- gsub("mind", "Mind", res_main$outcome_SOC)
# res_main$outcome_SOC <- factor(res_main$outcome_SOC,
# levels=c('Theory of Mind (non-adjusted)',
# 'Theory of Mind (adjusted)',
# 'Empathy (non-adjusted)',
# 'Empathy (adjusted)',
# 'Facial Emotion Recognition (non-adjusted)',
# 'Facial Emotion Recognition (adjusted)',
# 'Non-facial Emotion Recognition (non-adjusted)',
# 'Non-facial Emotion Recognition (adjusted)',
# 'Everyday Social Skills (non-adjusted)',
# 'Everyday Social Skills (adjusted)'))
res_main[,c("beta", "SE", "tstat", "df", "p_Satt",
"p_Satt_bonferroni", "CI_L", "CI_U", "tau2", "I2",
"n_studies", "n_nexp_out" , "n_exp_out")] <- apply(
res_main[,c("beta", "SE", "tstat", "df", "p_Satt",
"p_Satt_bonferroni", "CI_L", "CI_U", "tau2", "I2",
"n_studies", "n_nexp_out" , "n_exp_out")], 2, function(x) round(x, 3))
res_main <- res_main[order(res_main$outcome_SOC), ]
DT::datatable(res_main,
rownames = FALSE,
extensions = 'Buttons',
options = list( # options
buttons = c('copy', 'excel'),
scrollX = TRUE,
dom = c('ftB'),
autoWidth = TRUE,
columnDefs = list(
list(className = 'dt-center',
targets = "_all"))))Forest plot
res_main$SE_COR = (res_main$CI_U - res_main$CI_L) / (3.92)
res_main$raw_outcome_save = res_main$raw_outcome
# res_main$raw_outcome = res_main$raw_outcome_save
res_main$raw_outcome <- factor(res_main$raw_outcome,
levels=c('Theory of mind',
'Empathy',
'Facial emotion recognition',
'Non-facial emotion recognition',
'Everyday social skills'))
tab.plot <- data.frame(
Outcome = do.call(rbind, lapply(res_main$outcome_SOC,
function(x) paste(strwrap(x, width = 60),
collapse = "\n"))),
n_ADHD = res_main$n_exp_out,
n_nexpT = res_main$n_nexp_out,
n_studies = res_main$n_studies,
tau2 = res_main$tau2,
I2 = paste0(round(res_main$I2), "%"))
value_num_plot = data.frame(apply(res_main[, c("beta", "SE_COR")], 2, function(x) as.numeric(as.character(x))))
forest_modif(x = value_num_plot[ ,c("beta", "SE_COR")],
variant = "classic",
col = "Greys", xlab = "SMD", annotate_CI = TRUE,
study_table = tab.plot,
group = res_main$raw_outcome,
type = "study_only",
text_size = 3.5,
x_limit = c(-0.5, 1.5),
N = tab.plot$n_ADHD + tab.plot$n_nexpT,
x_breaks = seq(-3, 3, 0.5)
)
Diagnostics
# , eval = FALSE
# windows() ## create window to plot your file, eval = FALSE
metafor::profile.rma.mv(SCE)
# dev.off()Supplement 9. Supplementary analyses
9.1. Other statistical approaches
A. Borenstein
dat.borenstein.analysis = subset(dat.borenstein, es_adj == " Non-Adjusted")
meta.reg <- metafor::rma.mv(yi = es, V = se^2,
data = dat.borenstein.analysis,
random = ~ outcome_SOC | ID_study,
struct = "DIAG",
mods = ~ outcome_SOC - 1)
res.S1.a.clean = data.frame(
beta = meta.reg$beta,
se = meta.reg$se,
ci_lo = meta.reg$ci.lb,
ci_up = meta.reg$ci.ub,
pval = meta.reg$pval,
target = gsub("outcome_SOC", "", row.names(meta.reg$b)))
res.S1 <- left_join(res.S1.a.clean, dat_SOC)## Joining, by = "target"DT::datatable(res.S1,
rownames = FALSE,
extensions = 'Buttons',
options = list( # options
buttons = c('copy', 'excel'),
scrollX = TRUE,
dom = c('ftB'),
autoWidth = TRUE,
columnDefs = list(
list(className = 'dt-center',
targets = "_all"))))B. RVE approach
robu.main <- robumeta::robu(es ~ outcome_SOC - 1,
data = dat.crude,
studynum = ID_study,
var.eff.size = se^2,
modelweights = "CORR",
small = TRUE,
rho = 0.8)
dat_robu_main = data.frame(
beta = robu.main$reg_table$b.r,
se = robu.main$reg_table$SE,
ci_lo = robu.main$reg_table$CI.L,
ci_up = robu.main$reg_table$CI.U,
pval = robu.main$reg_table$prob,
target = gsub("outcome_SOC", "", row.names(meta.reg$b)),
target2 = robu.main$reg_table$labels)
DT::datatable(dat_robu_main,
rownames = FALSE,
extensions = 'Buttons',
options = list( # options
buttons = c('copy', 'excel'),
scrollX = TRUE,
dom = c('ftB'),
autoWidth = TRUE,
columnDefs = list(
list(className = 'dt-center',
targets = "_all"))))C. SCE vs RVE vs Borenstein
res.SCE.main = data.frame(
beta = res.SCE$beta,
se = res.SCE$SE,
ci_lo = res.SCE$CI_L,
ci_up = res.SCE$CI_U,
pval = res.SCE$p_Satt,
target =res.SCE$target)
res.SCE.main$Analysis = "SCE approach"
res.S1.a.clean$Analysis = "Borenstein approach"
dat_robu_main$Analysis = "RVE approach"
dat_forest_S1 = bind_rows(res.SCE.main,
res.S1.a.clean,
dat_robu_main)
meta_res_tot = meta::metagen(TE = beta, seTE = (ci_up-ci_lo)/(2*qnorm(.975)),
sm = "SMD", data = dat_forest_S1,
subgroup = target,
studlab = Analysis)
meta::forest(meta_res_tot, random = FALSE, fixed = FALSE,
leftcols = c("studlab"),
text.subgroup.nohet = FALSE, print.tau2 = FALSE, print.I2 = FALSE, print.pval.Q = FALSE,
plotwidth = "15cm", colgap.left = "1cm", colgap.right = "1cm",
leftlabs= c(""),
just = "right", spacing = 0.8)
9.2. Small study effects
A. Nakagawa approach
dat.crude$inv_n_tilda <- with(dat.crude, (n_nexp + n_exp)/(n_nexp*n_exp))
dat.crude$sqrt_inv_n_tilda <- with(dat.crude, sqrt(inv_n_tilda))
nak.pb = metafor::rma.mv(yi = es, V = V.SCE,
data = dat.crude,
mods = ~ 1 + sqrt_inv_n_tilda + outcome_SOC,
random = ~ outcome_SOC | ID_study,
struct = "DIAG",
test = "t",
control=list(optimizer="optim", optmethod="Nelder-Mead"),
sparse = TRUE)
clubSandwich::coef_test(nak.pb, vcov = "CR2", cluster = dat.crude$ID_study)## Coef. Estimate SE
## 1 intrcpt -0.693 0.545
## 2 sqrt_inv_n_tilda 4.848 1.893
## 3 outcome_SOCEveryday social skills (non-adjusted) 0.819 0.256
## 4 outcome_SOCFacial emotion recognition (non-adjusted) 0.121 0.255
## 5 outcome_SOCNon-facial emotion recognition (non-adjusted) -0.105 0.264
## 6 outcome_SOCTheory of mind (non-adjusted) 0.314 0.212
## t-stat d.f. p-val (Satt) Sig.
## 1 -1.272 14.5 0.22355
## 2 2.561 16.7 0.02046 *
## 3 3.199 13.6 0.00663 **
## 4 0.475 15.2 0.64163
## 5 -0.399 14.4 0.69559
## 6 1.479 14.7 0.16022B. PET-PEESE approach
PET = metafor::rma.mv(yi = es, V = V.SCE,
data = dat.crude,
mods = ~ se + outcome_SOC,
random = ~ outcome_SOC | ID_study,
struct = "DIAG",
sparse = TRUE)
clubSandwich::coef_test(PET, vcov = "CR2",
cluster = dat.crude$ID_study)## Coef. Estimate SE
## 1 intrcpt -1.2001 0.781
## 2 se 6.5829 2.806
## 3 outcome_SOCEveryday social skills (non-adjusted) 0.7548 0.228
## 4 outcome_SOCFacial emotion recognition (non-adjusted) 0.1471 0.236
## 5 outcome_SOCNon-facial emotion recognition (non-adjusted) 0.0225 0.266
## 6 outcome_SOCTheory of mind (non-adjusted) 0.2486 0.199
## t-stat d.f. p-val (Satt) Sig.
## 1 -1.5369 11.98 0.15030
## 2 2.3459 8.42 0.04547 *
## 3 3.3091 13.95 0.00519 **
## 4 0.6219 16.20 0.54265
## 5 0.0846 15.05 0.93371
## 6 1.2495 15.30 0.23024dat.crude$var = dat.crude$se^2
PET = metafor::rma.mv(yi = es, V = V.SCE,
data = dat.crude,
mods = ~ var + outcome_SOC,
random = ~ outcome_SOC | ID_study,
struct = "DIAG",
sparse = TRUE)
clubSandwich::coef_test(PET, vcov = "CR2",
cluster = dat.crude$ID_study)## Coef. Estimate SE
## 1 intrcpt 0.241 0.343
## 2 var 4.206 3.639
## 3 outcome_SOCEveryday social skills (non-adjusted) 0.676 0.213
## 4 outcome_SOCFacial emotion recognition (non-adjusted) 0.100 0.221
## 5 outcome_SOCNon-facial emotion recognition (non-adjusted) -0.178 0.239
## 6 outcome_SOCTheory of mind (non-adjusted) 0.256 0.178
## t-stat d.f. p-val (Satt) Sig.
## 1 0.705 11.05 0.4956
## 2 1.156 2.66 0.3409
## 3 3.169 13.49 0.0071 **
## 4 0.454 15.19 0.6565
## 5 -0.745 15.12 0.4677
## 6 1.441 13.88 0.1719C. Egger’s test for each outcome
dat_egger = data.frame(
meta_review = "Haza",
study = dat.borenstein.analysis$ID_study,
factor = dat.borenstein.analysis$outcome_SOC,
value = dat.borenstein.analysis$es,
se = dat.borenstein.analysis$se,
n_cases = dat.borenstein.analysis$n_exp,
n_controls = dat.borenstein.analysis$n_nexp,
measure = "G")
umb = metaumbrella::umbrella(dat_egger)## Analyzing factor: Theory of mind (non-adjusted)
## Analyzing factor: Everyday social skills (non-adjusted)
## Analyzing factor: Facial emotion recognition (non-adjusted)
## Analyzing factor: Non-facial emotion recognition (non-adjusted)
## Analyzing factor: Empathy (non-adjusted)umb##
## Umbrella review:
## Factor n_studies total_n n_cases
## 1 Theory of mind (non-adjusted) 49 4449 2284
## 2 Everyday social skills (non-adjusted) 82 10770 5570
## 3 Facial emotion recognition (non-adjusted) 42 3369 1712
## 4 Non-facial emotion recognition (non-adjusted) 8 707 381
## 5 Empathy (non-adjusted) 12 916 436
## n_controls measure value value_CI eG eG_CI eOR
## 1 2165 G 0.834 [0.68, 0.988] 0.834 [0.68, 0.988] 4.539
## 2 5200 G 1.224 [1.081, 1.366] 1.224 [1.081, 1.366] 9.201
## 3 1657 G 0.629 [0.46, 0.798] 0.629 [0.46, 0.798] 3.129
## 4 326 G 0.323 [0.076, 0.57] 0.323 [0.076, 0.57] 1.795
## 5 480 G 0.563 [0.149, 0.977] 0.563 [0.149, 0.977] 2.776
## eOR_CI p_value I2 PI_eG PI_eOR egger_p
## 1 [3.434, 5.998] 2.11e-26 78.946 [-0.145, 1.813] [0.769, 26.795] 4.03e-01
## 2 [7.1, 11.923] 3.18e-63 88.756 [0.006, 2.441] [1.011, 83.755] 7.16e-05
## 3 [2.303, 4.252] 3.04e-13 86.203 [-0.398, 1.656] [0.486, 20.141] 7.97e-01
## 4 [1.147, 2.811] 1.05e-02 60.343 [-0.42, 1.066] [0.467, 6.909] 4.20e-02
## 5 [1.311, 5.881] 7.67e-03 85.329 [-1.026, 2.152] [0.155, 49.577] 2.66e-01
## ESB_p power_med JK_p largest_CI_eG largest_CI_eOR rob amstar
## 1 5.47e-01 100 4.66e-25 [0.478, 1.536] [2.379, 16.202] NA NA
## 2 1.42e-02 100 2.41e-61 [0.421, 0.722] [2.145, 3.707] NA NA
## 3 9.48e-01 100 1.75e-12 [1.684, 2.302] [21.228, 65.088] NA NA
## 4 4.10e-01 100 3.37e-02 [-0.282, 0.333] [0.6, 1.83] NA NA
## 5 6.64e-01 100 1.95e-02 [0.02, 0.77] [1.036, 4.044] NA NA9.3. Type of publication
dat.publi = subset(dat.crude, Type_publication %in%
c("Article", "Thèse"))
V.SCE.publi <- with(dat.publi,
clubSandwich::impute_covariance_matrix(
vi = se^2,
cluster = ID_study,
r = 0.8,
return_list = FALSE,
smooth_vi = TRUE,
subgroup = outcome_SOC))
SCE.publi <- metafor::rma.mv(yi = es, V = V.SCE.publi,
data = dat.publi,
mods = ~ Type_publication,
random = ~ outcome_SOC | ID_study,
struct = "DIAG",
sparse = TRUE)
clubSandwich::coef_test(SCE.publi, vcov = "CR2", cluster = dat.publi$ID_study)## Coef. Estimate SE t-stat d.f. p-val (Satt) Sig.
## 1 intrcpt 0.875 0.057 15.340 94.4 <0.001 ***
## 2 Type_publicationThèse 0.100 0.131 0.765 10.4 0.461SCE.publi <- metafor::rma.mv(yi = es, V = V.SCE.publi,
data = dat.publi,
mods = ~ Type_publication - 1,
random = ~ outcome_SOC | ID_study,
struct = "DIAG",
sparse = TRUE)
clubSandwich::coef_test(SCE.publi, vcov = "CR2", cluster = dat.publi$ID_study)## Coef. Estimate SE t-stat d.f. p-val (Satt) Sig.
## 1 Type_publicationArticle 0.875 0.057 15.34 94.35 <0.001 ***
## 2 Type_publicationThèse 0.975 0.118 8.25 8.39 <0.001 ***SCE.publi <- metafor::rma.mv(yi = es, V = V.SCE.publi,
data = dat.publi,
mods = ~ Type_publication - 1,
random = ~ outcome_SOC | ID_study,
struct = "DIAG",
sparse = TRUE)
clubSandwich::coef_test(SCE.publi, vcov = "CR2", cluster = dat.publi$ID_study)## Coef. Estimate SE t-stat d.f. p-val (Satt) Sig.
## 1 Type_publicationArticle 0.875 0.057 15.34 94.35 <0.001 ***
## 2 Type_publicationThèse 0.975 0.118 8.25 8.39 <0.001 ***SCE.publi <- metafor::rma.mv(yi = es, V = V.SCE.publi,
data = dat.publi,
mods = ~ Type_publication:outcome_MOD - 1,
random = ~ outcome_SOC | ID_study,
struct = "DIAG",
sparse = TRUE)
clubSandwich::coef_test(SCE.publi, vcov = "CR2", cluster = dat.publi$ID_study)## Coef. Estimate SE
## 1 Type_publicationArticle:outcome_MODEveryday social skills 1.153 0.0745
## 2 Type_publicationThèse:outcome_MODEveryday social skills 1.498 0.1820
## 3 Type_publicationArticle:outcome_MODSocial cognition 0.691 0.0747
## 4 Type_publicationThèse:outcome_MODSocial cognition 0.692 0.2263
## t-stat d.f. p-val (Satt) Sig.
## 1 15.49 69.20 <0.001 ***
## 2 8.23 7.80 <0.001 ***
## 3 9.26 55.89 <0.001 ***
## 4 3.06 6.27 0.0211 *9.4. Adjustment for covariates
V.SCE.adjusted <- with(dat.adjusted,
clubSandwich::impute_covariance_matrix(
vi = se^2,
cluster = ID_study,
r = 0.8,
return_list = FALSE,
smooth_vi = TRUE,
subgroup = outcome_SOC))
SCE.adjusted <- metafor::rma.mv(yi = es, V = V.SCE.adjusted,
data = dat.adjusted,
mods = ~ outcome_SOC - 1,
random = ~ outcome_SOC | ID_study,
struct = "DIAG",
sparse = TRUE)
clubSandwich::coef_test(SCE.adjusted, vcov = "CR2", cluster = dat.adjusted$ID_study)## Coef. Estimate SE t-stat
## 1 outcome_SOCEmpathy (adjusted) 0.701 0.4286 1.64
## 2 outcome_SOCEveryday social skills (adjusted) 1.416 0.1991 7.11
## 3 outcome_SOCFacial emotion recognition (adjusted) 0.533 0.2055 2.59
## 4 outcome_SOCNon-facial emotion recognition (adjusted) 0.166 0.0282 5.89
## 5 outcome_SOCTheory of mind (adjusted) 0.848 0.1540 5.51
## d.f. p-val (Satt) Sig.
## 1 2.00 0.2438
## 2 7.90 <0.001 ***
## 3 5.98 0.0412 *
## 4 1.00 0.1071
## 5 6.84 <0.001 ***9.5. Type of tools
dat.tool = subset(dat.crude, !is.na(Type_mesure) & outcome_SOC== "Theory of mind (non-adjusted)")
dat.tool$vi = dat.tool$se^2
dat.tool$es_id = 1:nrow(dat.tool)
SCE.S11 <- metafor::rma.mv(yi = es, V = vi,
data = dat.tool,
mods = ~ Type_mesure ,
random = ~ 1 | ID_study/es_id)
clubSandwich::coef_test(SCE.S11, vcov = "CR2", cluster = dat.tool$ID_study)## Coef. Estimate SE t-stat d.f. p-val (Satt) Sig.
## 1 intrcpt 0.781 0.0794 9.83 45.00 <0.001 ***
## 2 Type_mesureReport 0.478 0.2348 2.04 7.34 0.0794 .SCE.S11wi <- metafor::rma.mv(yi = es, V = vi,
data = dat.tool,
mods = ~ Type_mesure - 1,
random = ~ 1 | ID_study/es_id)
clubSandwich::coef_test(SCE.S11wi, vcov = "CR2", cluster = dat.tool$ID_study)## Coef. Estimate SE t-stat d.f. p-val (Satt) Sig.
## 1 Type_mesureParadigm 0.781 0.0794 9.83 45.00 <0.001 ***
## 2 Type_mesureReport 1.259 0.2345 5.37 8.99 <0.001 ***9.6. Risk of bias
dat.risk = subset(dat.crude,
Risque_biais_inclusion == "1")
V.SCE.risk <- with(dat.risk,
clubSandwich::impute_covariance_matrix(
vi = se^2,
cluster = ID_study,
r = 0.8,
return_list = FALSE,
smooth_vi = TRUE,
subgroup = outcome_SOC))
SCE.risk <- metafor::rma.mv(yi = es, V = V.SCE.risk,
data = dat.risk,
mods = ~ outcome_SOC - 1,
random = ~ outcome_SOC | ID_study,
struct = "DIAG",
sparse = TRUE)
clubSandwich::coef_test(SCE.risk, vcov = "CR2", cluster = dat.risk$ID_study)## Coef. Estimate SE
## 1 outcome_SOCEmpathy (non-adjusted) 0.813 0.463
## 2 outcome_SOCEveryday social skills (non-adjusted) 1.386 0.121
## 3 outcome_SOCFacial emotion recognition (non-adjusted) 0.771 0.154
## 4 outcome_SOCNon-facial emotion recognition (non-adjusted) 0.301 0.211
## 5 outcome_SOCTheory of mind (non-adjusted) 0.895 0.127
## t-stat d.f. p-val (Satt) Sig.
## 1 1.76 3.99 0.154
## 2 11.42 33.73 <0.001 ***
## 3 5.02 14.89 <0.001 ***
## 4 1.43 1.97 0.291
## 5 7.03 24.72 <0.001 ***9.7. Effect of comorbidities
R = dat.borenstein %>%
group_by(ID_study) %>%
slice(1)
sum(R$n_exp)## [1] 8095sum(R$n_nexp)## [1] 7785dat.crude.S7 = subset(dat.crude, comor_ASD == "no-ASD")
dat.crude.S7.ASD = subset(dat.crude, comor_ASD == "ASD")
dat.ajd.S7.ASD = subset(dat.adjusted, comor_ASD == "ASD")
unique(c(dat.crude.S7.ASD$ID_study, dat.ajd.S7.ASD$ID_study))## [1] "Azadi Sohi_2011" "Berenguer_2018" "Craig _2015" "Liu_2021"
## [5] "Mouti_2019" "Oerlemans_2014" "Sinzig_2008" "Stephens_2021"
## [9] "Stordeur_2019" "Suing_2014" "Aiello_2021" "Manteris_2011"
## [13] "NeuroIMAGE_study"length(unique(dat.crude.S7$ID_study))## [1] 133length(unique(dat.crude.S7.ASD$ID_study))## [1] 10V.SCE.S7 <- with(dat.crude.S7,
clubSandwich::impute_covariance_matrix(
vi = se^2,
cluster = ID_study,
r = 0.8,
return_list = FALSE,
smooth_vi = TRUE,
subgroup = outcome_SOC))
SCE.S7 <- metafor::rma.mv(yi = es, V = V.SCE.S7,
data = dat.crude.S7,
mods = ~ outcome_SOC,
random = ~ outcome_SOC | ID_study,
struct = "DIAG",
sparse = TRUE)
clubSandwich::coef_test(SCE.S7, vcov = "CR2",
cluster = dat.crude.S7$ID_study)## Coef. Estimate SE
## 1 intrcpt 0.5637 0.211
## 2 outcome_SOCEveryday social skills (non-adjusted) 0.5943 0.224
## 3 outcome_SOCFacial emotion recognition (non-adjusted) 0.0906 0.228
## 4 outcome_SOCNon-facial emotion recognition (non-adjusted) -0.2031 0.253
## 5 outcome_SOCTheory of mind (non-adjusted) 0.2476 0.183
## t-stat d.f. p-val (Satt) Sig.
## 1 2.669 10.9 0.0219 *
## 2 2.654 13.4 0.0195 *
## 3 0.397 14.6 0.6971
## 4 -0.803 16.6 0.4333
## 5 1.352 13.3 0.1991Raw data
rawdata <- readxl::read_excel("G:/.shortcut-targets-by-id/1aQ8-ovwu-kQaKU1pgnyTKuT3yuRGuhr2/Revue_cognition.sociale_TDAH/shared_data_Haza_et_al.xlsx") %>% filter(!is.na(author))
#
DT::datatable(rawdata,
rownames = FALSE,
extensions = 'Buttons',
options = list( # options
buttons = c('copy', 'excel'),
scrollX = TRUE,
dom = c('ftB'),
autoWidth = TRUE,
columnDefs = list(
list(className = 'dt-center',
targets = "_all"))))