[R-sig-ME] Adjusting for random recording intervals in glmer/poisson

Joshua Wiley jwiley.psych at gmail.com
Thu Jul 5 07:52:19 CEST 2012

Hi Dieter,

I do not think that I understand the question or problem very well.
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.

Again apologies if I grossly misunderstanding the issue.



On Wed, Jul 4, 2012 at 10:35 PM, Dieter Menne
<dieter.menne at menne-biomed.de> wrote:
> In a clinical study, events in patients were observed during multiple visits; on
> each visit, a continuous predictor variable for the poisson-distributed number
> of events was also available, it is the endpoint of the study.
> The following model would be suitable
> glmer(nevent~predictor + (1|subj),data=d, family=poisson)
> but there is a catch: the recording interval on each day varies randomly, not
> related to study parameters, from 30 to 60 minutes. The statistical consultant
> at the university recommended the conservative solution to truncate ALL records
> to the first 30 minutes, and discard the tails, but the PhD student who did the
> study was not too happy to loose all data beyond 30 minutes.
> A compromise would be to normalize all data to events/45 minutes (or
> median(duration)), assuming that the variance in duration is not too large.
> Is there a better way to factor out the nuisance parameter duration?
> Dieter
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Joshua Wiley
Ph.D. Student, Health Psychology
Programmer Analyst II, Statistical Consulting Group
University of California, Los Angeles

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