# NOT RUN {
### load data
dat <- get(data(dat.begg1989))
### turn trt and arms into factors and set reference levels
dat$trt <- relevel(factor(dat$trt), ref="CMO")
dat$arms <- relevel(factor(dat$arms), ref="2")
### create data frame with the treatment differences for the controlled studies
dat2 <- data.frame(yi = dat$yi[c(1,3,5,7)] - dat$yi[c(2,4,6,8)],
vi = dat$vi[c(1,3,5,7)] + dat$vi[c(2,4,6,8)])
### DerSimonian and Laird method using the treatment differences
res <- rma(yi, vi, data=dat2, method="DL", digits=2)
res
### Begg & Pilote (1991) model incorporating the uncontrolled studies
res <- rma.mv(yi, vi, mods = ~ trt, random = ~ 1 | study,
data=dat, method="ML", digits=2)
res
### model involving bias terms for the uncontrolled studies
res <- rma.mv(yi, vi, mods = ~ trt + trt:arms, random = ~ 1 | study,
data=dat, method="ML", digits=2)
res
### model with random treatment effect
res <- rma.mv(yi, vi, mods = ~ trt, random = list(~ 1 | study, ~ trt | study),
struct="UN", tau2=c(0,NA), rho=0, data=dat, method="ML", digits=2)
res
### model with random treatment effect, but with equal variances in both arms
res <- rma.mv(yi, vi, mods = ~ trt, random = list(~ 1 | study, ~ trt | study),
struct="CS", rho=0, data=dat, method="ML", digits=2)
res
# }
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