### META ANALYSIS EXERCISE OF 1  
##PACKAGES  
install.packages("haven")
install.packages("meta")
install.packages("metafor")
install.packages("rmeta")
install.packages("dmetar")

##ATTACH TO PACKAGES 
library(meta)
library(metafor)
library(rmeta)
library(haven)

##IMPORTING DATASET FROM STATA
setwd("S:/_STAFF/Gulser/R NOTES/META ANALYSIS/data")
dataset<-read_dta('all832525.dta')
attach(dataset)
dataset
summary(dataset)

###META ANALYSIS

m<-metacont(nTreat, meanTreat, sdTreat, nCont, meanCont, sdCont, studlab=paste(Author, year), data=dataset,prediction=TRUE, sm="SMD")

s1<- summary(m)
s1
s2<-summary(update(m, hakn=TRUE)) ###hakn=A logical indicating whether the method by Hartung and Knapp should be used to adjust test statistics and confidence intervals.
s2

vars<-c("TE", "lower", "upper")
vars

res.md<-rbind(data.frame(s1$fixed)[vars], data.frame(s1$random)[vars], data.frame(s2$random)[vars])
res.md
res.md<-round(res.md, 5)
row.names(res.md)<-c("TE", "RE", "RE (HaKn)")
names(res.md)<-c("Absolute difference", "CI lower", "CI upper")
res.md

#RADIAL PLOT
radial(m)

##FOREST PLOT
forest(m)
forest(m, xlim = c(-2.5,7.5))

##FUNNEL PLOT
funnel(m)
funnel(m,xlab = "Hedges' g")
funnel(m,xlab = "g",studlab = TRUE)

funnel(m, xlab="Hedges' g", 
       contour = c(.95,.975,.99),
       col.contour=c("darkblue","blue","lightblue"))+
legend(1.4, 0, c("p < 0.05", "p<0.025", "< 0.01"),bty = "n",
       fill=c("darkblue","blue","lightblue"))


##BIAS
metabias(m, k.min=9)
metabias(m, k.min=9, method="Egger")
metabias(m, k.min=9, method="Thompson")

trimfill(m)