Adjusting for random recording intervals in glmer/poisson

What is the significance of the recording interval varying?  If the
issue is that with a longer recording time, there are more
opportunities for events to occur, then what about treating duration as
an exposure and including it in the offset?  Essentially you model rate
then rather than counts.

Good to hear that you suggest it to put it into the offset; I wanted to do this, but was not sure what exactly to put into the offset term. Duration or log(duration)?

Dieter


Apologies: I forgot to attach the simulated sample data in the original message

library(lme4)
nsubj = 10
nvisit = 5
set.seed(100)
d = data.frame(
  subj = as.factor(1:nsubj),
  duration = runif(nsubj*nvisit,30,60),# in minutes
  predictor = rnorm(nsubj*nvisit,50,10))
d$nevent = with(d,rpois(nsubj*nvisit,predictor*duration/500))

# Proposed solution by university statistician: 
# use only the data from the first 30 minutes (not shown here) and do
glmer(nevent~predictor + (1|subj),data=d, family=poisson)
# Result is not correct, because truncated data not used

# Proposed by Joshua
glmer(nevent~predictor+offset(log(duration)) + (1|subj), data=d, family=poisson)