GES_chap11.txt

model {
 
 # Randomised Control Trials:
 for (i in 1:R) {
 # Likelihood
  rct.rd[i] ~ dnorm(rct.psi[i], rct.prec[i])
  # Random effects model
  rct.psi[i] <- theta[1] + (rct.z[i] * sd.theta[1])
  # Prior
  rct.z[i] ~ dnorm(0, 0.001)
  # Transformation
  rct.prec[i] <- 1 / (rct.serd[i] * rct.serd[i])
 }
 
 # Comparative cohort studies:
 for (i in 1:C) {
# Likelihood
  coh.rd[i] ~ dnorm(coh.psi[i], coh.prec[i])
  # Random effect model
  coh.psi[i] <- theta[2] + (coh.z[i] * sd.theta[2])
  # Prior
  coh.z[i] ~ dnorm(0, 0.001)
  # Transformation
  coh.prec[i] <- 1 / (coh.serd[i] * coh.serd[i])
 }
 
 # Before and after studies:
 for (i in 1:B) {
 # Likelihood
  ba.rd[i] ~ dnorm(ba.psi[i], ba.prec[i])
  # Random effects model
  ba.psi[i] <- theta[3] + (ba.z[i] * sd.theta[3])
  # Prior
  ba.z[i] ~ dnorm(0, 0.001)
  # Transformation
  ba.prec[i] <- 1 / (ba.serd[i] * ba.serd[i])
 }
 
 # Combining all 3 sources of information:
 for (i in 1:T) {
 # Regression effect model
  theta[i] <- mean + (u[i] * sd.mean)
  u[i] ~ dnorm(0, 1 / u.prec[i]^2)
  # Prior
  u.prec[i] ~ dunif(0, 1)
  # Prior
  sd.theta[i] ~ dnorm(0, 0.001)T(0, )
  # Transformation
  var.theta[i] <- sd.theta[i] * sd.theta[i]
  # Transformation
  prec.theta[i] <- 1 / (sd.theta[i] * sd.theta[i])
  
 }
 # Hyperpriors:
 mean ~ dnorm(0, mean.prec)
 sd.mean ~ dnorm(0, 1 / sd.prec^2)T(0, )
 var.mean <- sd.mean * sd.mean
 prec.mean <- 1 / (sd.mean * sd.mean)
 sd.prec ~ dunif(0, 1)
 mean.prec ~ dunif(0, 1)
 
 mean.Pr <- exp(mean)
 }