df.summary.brut1<-bind_rows(
cbind(df.Meta.Prim, Analysis=rep("Primary", 4)),
cbind(df.Meta.S1, Analysis=rep("S1: CS structure", 4)),
cbind(df.Meta.S2, Analysis=rep("S2: Separate Meta-analyses", 4)),
cbind(df.Meta.S4, Analysis=rep("S4: Cook's distance", 4)),
cbind(df.Meta.S5, Analysis=rep("S5: High leverage", 4)),
cbind(df.Meta.S6, Analysis=rep("S6: Without short duration", 4)),
cbind(df.Meta.S7, Analysis=rep("S7: On all participants", 4))
)
df.summary.brut2<- df.summary.brut1 %>%
mutate(
Raw.r=round(as.numeric(Raw.r), digits=2),
Adj.CIlow=round(Adj.CIlow, digits=2),
Adj.CIup=round(Adj.CIup, digits=2),
p.val=round(p.val , digits=2),
Adj.p.val=round( Adj.p.val, digits=2),
Cochran.Q=round( Cochran.Q, digits=2),
Cochran.Q.p.val=round( Cochran.Q.p.val, digits=2),
TOST=round(TOST, digits=2))
df.Meta.S3.recode<- data.frame(
Study=rep("Pooled Effect Size",4),
Outcome=rep("All outcomes",4),
SleepIndicator=c("Quality of Acute Sleep", "Quality of Chronic Sleep",
"Quantity of Acute Sleep","Quantity of Chronic Sleep"),
N=rep(sum(N.prim),4),
Raw.r=paste0("b=", round(df.Meta.S3$estimate[5:8],2)),
p.val=round(df.Meta.S3$p.value[5:8],2),
Adj.CIlow=round(df.Meta.S3$estimate[5:8]-df.Meta.S3$std.error[5:8]*qnorm(1-0.05/(2*4)),2),
Adj.CIup=round(df.Meta.S3$estimate[5:8]+df.Meta.S3$std.error[5:8]*qnorm(1-0.05/(2*4)),2),
Cochran.Q=rep(NA_real_, 4),
Cochran.Q.p.val=rep(NA_real_, 4),
TOST=rep(NA_real_, 4),
Analysis=rep("S3: One-stage model",4))
S3.Adj.p.val<- case_when(
df.Meta.S3.recode$p.val*4>.99~.99,
df.Meta.S3.recode$p.val*4<=.99~4*as.numeric(df.Meta.S3.recode$p.val))
df.Meta.S3.1<-cbind(df.Meta.S3.recode[,1:6], Adj.p.val=S3.Adj.p.val, df.Meta.S3.recode[,7:12])
df.summary<-rbind(
df.summary.brut2[1:12,],
df.Meta.S3.1,
df.summary.brut2[13:nrow(df.summary.brut2),]) %>%
dplyr::select(SleepIndicator, Analysis, N, Raw.r, Adj.CIlow, Adj.CIup, p.val, Adj.p.val, TOST)
colnames(df.summary)[c(1, 4:8)] <- c("Sleep Indicator",
"Pearson's r",
"Lower bound adjusted 95% CI",
"Upper bound adjusted 95% CI",
"Raw p-values",
"Adjusted p-values")