model
   {
   # transform collapsed data into full
      for (i in 1 : I){
         Y[i,1] <- 1 
         Y[i,2] <- 0
      }
   # loop around strata with case exposed, control not exposed (n10)
      for (i in 1 : n10){ 
         est[i,1] <- 1 
         est[i,2] <- 0
      }
   # loop around strata with case not exposed, control exposed (n01)
      for (i in (n101) : (n10+n01)){ 
         est[i,1] <- 0 
         est[i,2] <- 1
      }
   # loop around strata with case exposed, control exposed (n11)
      for (i in (n10+n011) : (n10+n01+n11)){ 
         est[i,1] <- 1 
         est[i,2] <- 1
      }
   # loop around strata with case not exposed, control not exposed (n00)
      for (i in (n10+n01+n111) :I ){ 
         est[i,1] <- 0 
         est[i,2] <- 0
      }

   # PRIORS
      beta ~ dnorm(0,1.0E-6) ; 
   
   # LIKELIHOOD
      for (i in 1 : I) { # loop around strata   
   # METHOD 1 - logistic regression
   # Y[i,1] ~ dbin( p[i,1], 1) 
   # logit(p[i,1]) <- beta * (est[i,1] - est[i,J]) 
   
   # METHOD 2 - conditional likelihoods
          Y[i, 1 : J] ~ dmulti( p[i, 1 : J],1)
         for (j in 1:2){
            p[i, j] <- e[i, j] / sum(e[i, ])
            log( e[i, j] ) <- beta * est[i, j] 
         } 
   # METHOD 3 fit standard Poisson regressions relative to baseline
    #for (j in 1:J) { 
    #   Y[i, j] ~ dpois(mu[i, j]);
    #   log(mu[i, j]) <- beta0[i] + beta*est[i, j]; 
       } 
    #beta0[i] ~ dnorm(0, 1.0E-6) 
   }