#Bayesian analysis using Pocock's method with piecewise constant baseline hazard
model {  
 ## Likelihoods ##
    for (i in 1:N) {
  	      # define the likelihood
  			  mu[i] <- TimeIntervals[i]*exp(alpha[Interval[i]]+inprod(par[],X[i,])+delta[Trial[i]])
		  	  EventsIntervals[i] ~ dpois(mu[i]);
	   } 
  #Variance and precision of study-specific biases
  sigmastudy2 <- sigmastudy*sigmastudy
  tau<- 1/sigmastudy2

  #Delta's (trial specific biases) are normally distributed. Here we set the population mean of the theta's to zero,
  #because the alpha's already describe the baseline hazard.
    for (i in minTrial:(maxTrial-1)){
      delta[i] ~ dnorm(0, tau)
  } 
  #No bias for current data
  delta[maxTrial]<- 0

	#Correlated Priors on Log-Hazards
	  for (j in 1:P){
		 par[j] ~ dnorm(0,0.0001);
	  }
	  alpha[1] ~ dnorm(0,0.0001);
	  for (k in 2:K) {
		 alpha[k] ~ dnorm(alpha[k-1], eta);
	  }
		eta <- 1/(sigma*sigma);
		sigma ~ dunif(0.01,100);
}