model{
for(i in 1:N.c){   
    r.acr.c[i] ~ dbin(p.c[i], n.c[i])
    logit(p.c[i]) <- mu[study.c[i]]+ beta*mtx.c[i]
}

for(i in 1:N.t){ 
    r.acr.t[i] ~ dbin(p.t[i], n.t[i])
    logit(p.t[i]) <- mu[study.t[i]] + beta*mtx.t[i]+ lor[i]
    lor[i] <- theta[treat.t[i]]
    fit.lor[i]<-lor[i]+beta*(mtx.t[i]-mtx.c[study.t[i]])
}

#random effect for all treatments
for(i in 1:N.t){ 
    theta[i] ~ dnorm(re.mean[i], tau.theta)
    re.mean[i] <- mu.theta[drug.t[i]]
}
tau.theta<-1/ss.theta
ss.theta<-s.theta*s.theta

#priors
for(i in 1:N.c){mu[i] ~ dnorm(0, 1.0E-6)}
for(i in 1:4){mu.theta[i]~dnorm(0, 1.0E-6)}
s.theta~dunif(0.01, 5)
beta ~ dnorm(0, 1.0E-6) 

#transformed variables
for(i in 1:4){exp.mu.theta[i]<-exp(mu.theta[i])}

# unused variables
dur.s.bar<-mean(dur.s[])
haq.b.s.bar<-mean(haq.b.s[])
drug.s.bar<-mean(drug.s[])
tnf.s.bar<-mean(tnf.s[])
}