model
{
# Set up data
for(i in 1:N) {
for(j in 1:T) {
# risk set = 1 if obs.t >= t
Y[i,j] <- step(obs.t[i] - t[j] + eps)
# counting process jump = 1 if obs.t in [ t[j], t[j+1] )
# i.e. if t[j] <= obs.t < t[j+1]
dN[i, j] <- Y[i, j] * step(t[j + 1] - obs.t[i] - eps) * fail[i]
}
}
# Model
for(j in 1:T) {
for(i in 1:N) {
dN[i, j] ~ dpois(Idt[i, j]) # Likelihood
Idt[i, j] <- Y[i, j] * exp(beta * Z[i]) * dL0[j] # Intensity
}
dL0[j] ~ dgamma(mu[j], c)
mu[j] <- dL0.star[j] * c # prior mean hazard
# Survivor function = exp(-Integral{l0(u)du})^exp(beta*z)
S.treat[j] <- pow(exp(-sum(dL0[1 : j])), exp(beta * -0.5));
S.placebo[j] <- pow(exp(-sum(dL0[1 : j])), exp(beta * 0.5));
}
c <- 0.001
r <- 0.1
for (j in 1 : T) {
dL0.star[j] <- r * (t[j + 1] - t[j])
}
beta ~ dnorm(0.0,0.000001)
}