model{
for(i in 1:NS)
{
w[i,1] <- 0
delta[i,t[i,1]] < - 0
mu ~ dnorm(0,.0001)
for (k in 1:na)
{
r[i,k] ~dbin(p[i,t[i,k]],n[i,k])
logit(p[i,t[i,k]]) <- mu+delta[i,t[i,k]]}
for (k in 2:na)
{ delta[i,t[i,k]] ~ dnorm(md[i,t[i,k]],taud[i,t[i,k]])
md[i,t[i,k]] <- d[t[i,k]] - d[t[i,1]] + sw[i,k]
taud[i,t[i,k]] < - tau *2*(k -1)/k
w[i,k] <- (delta[i,t[i,k]] - d[t[i,k]] + d[t[i,1]])
sw[i,k] < - sum(w[i,1:k -1])/(k-1)
}
}
d[1]<-0
for (k in 2:NT) { d[k] ~ dnorm(0,.0001) }
sd ~ dunif(0,2)
tau <- pow(sd,2)
for (k in 1:NT) { rk[k]<-NT+1-rank(T[],k)
best[k] < - equals(rk[k],1)
}
for (k in 1:NT){
  order[k]<-rank(d[],k)
most.effective[k]<-equals(order[k],1)
for (j in 1:NT){
   effectiveness[k,j]<-equals(order[k],j)}
}
for (k in 1:NT){
   for (j in 1:NT){
cumeffectiveness[k,j]<-sum(effectiveness[k,1:j])}
}
for (k in 1:NT){
   SUCRA[k]<-sum(cumeffectiveness[k,1:(NT-1)])/(NT-1)
}
for (c in 1:(NT-1)){
for (k in (c+1):NT){
lor[c,k]<-d[k]-d[c]
  log(or[c,k])<-lor[c,k]
}
}
}