model {

for(i in 1:sN) {

p[i]<-phi(mu[t[i]]+ vi[s[i], t[i]]) # model

r[i]∼dbin(p[i], totaln[i]) # binomial likelihood

}

for(j in 1:tS){

vi[j, 1:tN]∼dmnorm(mn[1:tN], T[1:tN,1:tN]) # multivariate normal distribution

}

invT[1:tN, 1:tN]<-inverse(T[ , ])

for (j in 1:tN){

mu[j]∼dnorm(0, 0.001)

sigma[j]<-sqrt(invT[j, j])

probt[j]<-phi(mu[j]/sqrt(1+invT[j, j]))

# population-averaged treatment specific event rate

}

T[1:tN,1:tN] ∼ dwish(R[1:tN, 1:tN], tN) # Wishart prior

for (k in 1:tN) {

rk[k]<- tN + 1 - rank(probt[],k) # ranking

best[k]<-equals(rk[k],1) # prob {treatment k is best}

}

for (j in 1:tN){ # calculation of RR, RD and OR

for (k in (j+1):tN){

RR[j, k] <- probt[j]/probt[k]

RD[j, k] <- probt[j]-probt[k]

OR[j, k] <- probt[j]/(1-probt[j])/probt[k]*(1-probt[k])

}

}

}