### Read and format the data (binary)
data = read.csv(url("http://www.statistica.it/gianluca/bmeta/Data-bin.csv"))
### List data for binary outcome
data.list <- list(y0=data$y0,y1=data$y1,n0=data$n0,n1=data$n1)
### Select fixed-effects meta-analysis with normal prior for binary data
x <- bmeta(data.list, outcome="bin", model="std.norm", type="fix")
### Plot forest plot
forest.plot(x)
### Plot forest plot on log scale
forest.plot(x,log=TRUE)
### Select random-effects meta-analysis with t-distribution prior for binary
### data
x <- bmeta(data.list, outcome="bin", model="std.dt", type="ran")
### Plot 'two-line' forest plot showing estimates from both randome-effects
### model and no-pooling effects model for comparison
forest.plot(x,add.null=TRUE,title="Two-line forestplot for comparison")
### Read and format the data (continuous)
data = read.csv(url("http://www.statistica.it/gianluca/bmeta/Data-ctns.csv"))
### List data for continuous outcome
data.list <- list(y0=data$y0,y1=data$y1,se0=data$se0,se1=data$se1)
### Select fix-effects meta-analysis for studies reporting two arms separately
x <- bmeta(data=data.list,outcome="ctns",model="std.ta",type="fix")
### Define for individual studies
study.label <- c(paste0(data$study,", ",data$year),"Summary estimate")
### Produce forest plot with label for each study and control the lower and upper
### limits for clipping credible intervals to arrows
forest.plot(x,study.label=study.label,clip=c(-7,4))
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