I am fitting a continual reassessment (CRM) model that has a normal
prior for alpha and a logistic dose response curve. The following model
works:
model
{
prec <- 1/pow(priorsd,2)
alpha ~ dnorm(priormn, prec)
for (i in 1:J)
{
y[i] ~ dbern(p[i])
logit(p[i]) <- 3 + alpha*d[i]
}
}
I am then trying to fit a model where each observation is weighted by
w[i] (this is a time-to-event CRM). I tried modifying the last few
lines as follows, but I am not getting reasonable results:
logit(p[i]) <- 3 + alpha*d[i]
wp[i] <- w[i]*p[i]
y[i] ~ dbern(wp[i])
Does anyone know how to do this?
Thanks,
Bill
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