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 (n10+1):(n10+n01)){  est[i,1] <- 0     est[i,2] <- 1}
	# loop around strata with case exposed, control   exposed  (n11)
		for (i in (n10+n01+1):(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+n11+1):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) 
	}

}
