Model.5.2.2.txt

model {
   # Binomial Likelihood for the 
   # i'th study:
   for (i in 1:Nstud) {
    rA[i] ~ dbin(pA[i], nA[i])
    rB[i] ~ dbin(pB[i], nB[i])
  
   # Logistic regression for
   # the i'th study:
    logit(pA[i]) <- mu[i]
    logit(pB[i]) <- mu[i] + delta[i] + beta * (lat[i] - mean(lat[]))
  
   # Statistical model, 
   # linking the
   # parameters to likelihood:
    delta[i] ~ dnorm(d, prec)
  
  }
 # Prior on µ, the estimate 
 # log(odds) of an event for 
 # i'th study:
  for (i in 1:Nstud) {
   mu[i] ~ dnorm(0.0, 1.0e-5)
  
  }
 # Prior on intercept (d), 
 # the mean pooled treatment 
 # effect:
  d ~ dnorm(0.0, 1.0e-6)
 # Uncentering d:
  d.uncent <- d - beta * mean(lat[])
 # Data transformation for d:
  OR <- exp(d)
  
 # Prior on tau, sd for
 # between studies:
  tau ~ dunif(0, 10)
 # tau.sq, between-study 
 # variance:
  tau.sq <- tau * tau
 # Precision as a 
 # fucntion of tau:
  prec <- 1 / (tau.sq)
  
 # Prior on Beta, the
 # coefficient:
  beta ~ dnorm(0.0, 1.0e-6)
}