Bayesian Methods in Health Economics

Health economic evaluation in practice 163

e0[i] ~ dnorm(mu.e[1],tau[1])

phi0[i] <- mu.c[1]+beta[1]*(e0[i]-mu.e[1])

}

# Treatments

for(i in 1:n[2]){

c1[i] ~ dgamma(eta[2],lambda1[i])

lambda1[i] <- eta[2] / phi1[i]

e1[i] ~ dnorm(mu.e[2],tau[2])

phi1[i] <- mu.c[2]+beta[2]*(e1[i]-mu.e[2])

}

for (t in 1:2) {

tau[t] <- pow(sigma.e[t],-2) # precision for QALYs

sigma2.e[t] <- pow(sigma.e[t],2) # variance for QALYs

sigma.e[t] <- exp(logsigma.e[t]) # st.dev. for QALYs

# Prior distributions

eta[t] ~ dunif(0,100) # shape parameter of Gamma dist.

mu.c[t] ~ dunif(0,2000) # mean cost (normal scale)

mu.e[t] ~ dnorm(0, 1.0E-6) # mean QALY (logit scale)

logsigma.e[t] ~ dunif(-5,10) ...