Model.6.3.txt

model {
    for (i in 1:32) {
     # Likelihood:
      r[i] ~ dbin(p[i], n[i])
      
     # Logistic regression model:
      logit(p[i]) <- mu[s[i]] + delta[s[i]] * equals(t[i], 2)
        
       # Predicted likelihood
       # for deaths:
        r.mxd[i] ~ dbin(p.new[i], n[i])
       
       # Mixed predictve 
       # p-value:
        p.mxd[i] <- step(r.mxd[i] - r[i]) - 0.5 * equals(r.mxd[i], r[i])
        
       # Predicted probability
       # of death:
        logit(p.new[i]) <- mu[s[i]] + delta.new * equals(t[i], 2)

    }
  for (j in 1:16) {
   # Hierarchical random 
   # effects model:
    delta[j] ~ dnorm(d, prec) 
   
 # Prior for baseline
 # random effect, µ:
  mu[j] ~ dnorm(0, 1.0e-6)
  }
 
 # Prior for Pop. 
 # treatment effect:
  d ~ dnorm(0, 1.0e-6)
 # Population OR:
  OR <- exp(d)
 # Prior for btw. studies
 # sd:
  tau ~ dnorm(0, 1.0e-6)T(0, )
 # Variance:
  tau.sq <- (tau * tau)
 # Precision:
  prec <- 1 / (tau.sq)
 
 # Predicted average 
 # treatment effect:
  delta.new ~ dnorm(d, prec)
  
 # Mean random
 # effect:
  delta[19] <- exp(d)

}