model
{
# transform collapsed data into full
for (i in 1 : I){
Y[i,1] <- 1
Y[i,2] <- 0
}
# loop around strata with case exposed, control not exposed (n10)
for (i in 1 : n10){
est[i,1] <- 1
est[i,2] <- 0
}
# loop around strata with case not exposed, control exposed (n01)
for (i in (n10+1) : (n10+n01)){
est[i,1] <- 0
est[i,2] <- 1
}
# loop around strata with case exposed, control exposed (n11)
for (i in (n10+n01+1) : (n10+n01+n11)){
est[i,1] <- 1
est[i,2] <- 1
}
# loop around strata with case not exposed, control not exposed (n00)
for (i in (n10+n01+n11+1) :I ){
est[i,1] <- 0
est[i,2] <- 0
}
# PRIORS
beta ~ dnorm(0,1.0E-6) ;
# LIKELIHOOD
for (i in 1 : I) { # loop around strata
# METHOD 1 - logistic regression
# Y[i,1] ~ dbin( p[i,1], 1)
# logit(p[i,1]) <- beta * (est[i,1] - est[i,J])
# METHOD 2 - conditional likelihoods
Y[i, 1 : J] ~ dmulti( p[i, 1 : J],1)
for (j in 1:2){
p[i, j] <- e[i, j] / sum(e[i, ])
log( e[i, j] ) <- beta * est[i, j]
}
# METHOD 3 fit standard Poisson regressions relative to baseline
#for (j in 1:J) {
# Y[i, j] ~ dpois(mu[i, j]);
# log(mu[i, j]) <- beta0[i] + beta*est[i, j];
}
#beta0[i] ~ dnorm(0, 1.0E-6)
}