	model 
	{
		for(j in 1 : N) {
			for(k in 1 : T) {
				log(mu[j, k]) <- a0 + alpha.Base * (log.Base4[j] - log.Base4.bar)   
	                  + alpha.Trt * (Trt[j] - Trt.bar)  
	                  + alpha.BT  * (BT[j] - BT.bar)  
	                  + alpha.Age * (log.Age[j] - log.Age.bar)  
	                  + alpha.V4  * (V4[k] - V4.bar) 
	                  + b1[j] + b[j, k]
				y[j, k] ~ dpois(mu[j, k])
				b[j, k] ~ dnorm(0.0, tau.b);       # subject*visit random effects
			}
			b1[j]  ~ dnorm(0.0, tau.b1)        # subject random effects
			BT[j] <- Trt[j] * log.Base4[j]    # interaction
			log.Base4[j] <- log(Base[j] / 4) log.Age[j] <- log(Age[j])
		}
		
	# covariate means:
		log.Age.bar <- mean(log.Age[])                
		Trt.bar  <- mean(Trt[])                   
		BT.bar <- mean(BT[])                 
		log.Base4.bar <- mean(log.Base4[])         
		V4.bar <- mean(V4[])                  
	# priors:
	
		a0 ~ dnorm(0.0,1.0E-4) 		           
		alpha.Base ~ dnorm(0.0,1.0E-4)            
		alpha.Trt  ~ dnorm(0.0,1.0E-4);           
		alpha.BT   ~ dnorm(0.0,1.0E-4)            
		alpha.Age  ~ dnorm(0.0,1.0E-4)            
		alpha.V4   ~ dnorm(0.0,1.0E-4)
		tau.b1     ~ dgamma(1.0E-3,1.0E-3); sigma.b1 <- 1.0 / sqrt(tau.b1)
		tau.b      ~ dgamma(1.0E-3,1.0E-3); sigma.b  <- 1.0/  sqrt(tau.b)		     
		        
	# re-calculate intercept on original scale: 
		alpha0 <- a0 - alpha.Base * log.Base4.bar - alpha.Trt * Trt.bar 
		- alpha.BT * BT.bar - alpha.Age * log.Age.bar - alpha.V4 * V4.bar
	}
