# Longitudinal model over time
# Linear time-course function
# Fixed treatment effects

model{ 			# Begin Model Code
 
for(i in 1:NS){ # Run Through all NS trials
    
	mu[i] ~ dnorm(0,0.0001)
	delta[i,1] <- 0
	
	alpha[i] ~ dnorm(0,0.0001)	# Prior on baseline nuisance parameter
	
    for (k in 1:narm[i]){
		for (m in 1:fups[i]) {	# Run through all observations within a study	
			
			y[i,k,m] ~ dnorm(theta[i,k,m], prec[i,k,m])	# Normal likelihood
			prec[i,k,m] <- pow(se[i,k,m], -2)			
			
			theta[i,k,m] <- alpha[i] + (beta[i,k]*time[i,m])	# Time-course function
						
			resdev[i,k,m] <- pow((y[i,k,m] - theta[i,k,m]),2) * prec[i,k,m] # residual deviance for normal likelihood			
			dev[i,k,m] <- -2* (log(pow((prec[i,k,m]/(2*3.14159)),0.5) * exp(-0.5*(pow((y[i,k,m]-theta[i,k,m]),2)*prec[i,k,m])))) # deviance for normal likelihood
			
		}
		
		beta[i,k] <- mu[i] + delta[i,k]	# Relative effects for time-course parameters
		
		resarmdev[i,k] <- sum(resdev[i,k,1:fups[i]])
		armdev[i,k] <- sum(dev[i,k,1:fups[i]])
		
	}
	
	resstudydev[i] <- sum(resarmdev[i, 1:narm[i]])
	studydev[i] <- sum(armdev[i, 1:narm[i]])
		
	for(k in 2:narm[i]){
		delta[i,k] <- d[treat[i,k]] - d[treat[i,1]]		# Consistency equation
		}
		
	}
	
	d[1] <- 0
		
	for (k in 2:NT){
		d[k] ~ dnorm(0,0.0001)	# Priors on relative treatment effects	
		}
		
	totresdev <- sum(resstudydev[])
	totdev <- sum(studydev[])
	
	# Model ends
}