model {

for (i in 1:N) {

# Model specification

# y[i] is disease status

y[i]~dbern(delta)

# change outcome to 1 and 2 for matrix

# indexing rather than 0 and 1

ix[i] <- y[i] + 1

# joint distal and neck BMD conditional

# distributions, lgx is log(BMD), mu/tau are

# prior mean/var vectors of distal and neck BMD

lgx[i,1:2]~dmnorm(mu[ix[i],1:2],tau[ix[i],1:2,1:2])

}

smallnumber <- 1.0E-5

# theta is marginal probability of disease

# in study population

theta~dbeta(smallnumber, smallnumber)

for(j in 1:2) {

# hyper-parameters of Mean/Precision from R

mu[j,1:2]~dmnorm(Mean[],Prec[,])

# hyper-parameters of Omega/degFdm from R

tau[j,1:2,1:2]~dwish(Omega[,],degFdm)

}

}