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[study.c[i]]*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[study.t[i]]*mtx.t[i]+lor[i]
    lor[i] <- theta[study.t[i]]         
    fit.lor[i]<-lor[i]+beta[study.t[i]]*(mtx.t[i]-mtx.c[study.t[i]]) #fitted log odds ratio
}



#random effect for all treatments
for(i in 1:N.c){ 
    theta[i] ~dnorm(theta.mean[i],tau)
    beta[i] ~ dnorm(beta.mean[i],tau.beta)
    theta.mean[i] <- mu.theta[drug.s[i]]  #drug.s is drug used in study i
    beta.mean[i] <- mu.beta+1/2*(theta[i]-theta.mean[i])
}
tau<-1/sigma.sq
sigma.sq<-sigma*sigma
tau.beta<-4/3*tau

#priors
for(i in 1:N.c){ mu[i] ~ dnorm(0, 1.0E-6)}
mu.beta~dnorm(0, 1.0E-6)
for(i in 1:4){mu.theta[i]~dnorm(0, 1.0E-6)}
sigma~dunif(0,2)


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

#adjusted mean lor for various durations and treated with MTX and anti-TNF. average rf and haq.
for(t in 1:4){
    for(d in 1:16){
        adj.lor[d,t]<-mu.theta[t]
           adj.or[d,t]<-exp(adj.lor[d,t])
    }
}

#difference in log OR between treatments. first-second
for(i in 1:3){
    i1[i]<-study.c[i]+1
    for(j in (i+1):4){
        df.lor[j,i]<-mu.theta[j]-mu.theta[i]
        df.or[j,i]<-exp(df.lor[j,i])
    }
}

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