Other software, such as WinBUGS/OpenBUGS, can be useful for exploring a meta-analysis beyond the standard functions of StatsDirect. You might work with a statistician to build a Bayesian model of your meta-analysis and run simulations of it in WinBUGS.
A typical fixed effects odds ratio BUGS model:
model
{
for( i in 1 : Num ) {
rc[i] ~ dbin(pc[i], nc[i])
rt[i] ~ dbin(pt[i], nt[i])
logit(pc[i]) <- mu[i]
logit(pt[i]) <- mu[i] + d
mu[i] ~ dnorm(0.0,1.0E-6)
}
d ~ dnorm(0.0,1.0E-6)
or <- exp(d)
}
A typical random effects odds ratio BUGS model:
{
for( i in 1 : Num ) {
rc[i] ~ dbin(pc[i], nc[i])
rt[i] ~ dbin(pt[i], nt[i])
logit(pc[i]) <- mu[i]
logit(pt[i]) <- mu[i] + delta[i]
mu[i] ~ dnorm(0.0,1.0E-5)
delta[i] ~ dnorm(d, tau)
}
d ~ dnorm(0.0,1.0E-6)
# Choice of priors for random effects variance
#tau ~ dgamma(0.001,0.001)
#sigma <- 1 / sqrt(tau)
tau<-1/(sigma*sigma)
sigma~dunif(0,10)
delta.new ~ dnorm(d, tau)
or <- exp(d)
}
Warning – do not attempt this sort of modelling without the guidance of an expert statistician.
Meta-regression methods (such as the metareg macro for Stata) can be used to explore origins of heteroenegity in meta-analyses. To perform meta-regression you need: the effect estimate for each study; a confidence interval or standard error for the effect; and covariables that might explain the origins of the differences between the studies. There are numerous ways to go wrong in the use and interpretation of meta-regression, therefore please seek the help of a statistician (Thompson and Higgins, 2002).