Alex Pavluck <apavluck@GMAIL.COM> replied (to me):
> Thanks for your reply. The problem is that we want to make sure
> our main effects are not confounded by screening. So, to check this
> are running a model of only screeners and comparing it to the total
> population main effects. However, screening is a time dependent
> variable because we asked it three times. If they are screening in
> internal 1 and 3 but not 2 then they will only be considered during
> internal 1 and 3 and all person-time and covariate information will be
> dropped for interval 2. So, unless I am missing the obvious, it is
> so easy to drop missing because missing in interval 1 can be present
> interval 2.
It seems to me that you would want to include screening and
non-screening, so you *can* determine whether your main effects
are related to screening. If you only work with 'screening'
cases, I don't se how you can separate the effects.
The temporal features can complicate things, but I don't see that
you can safely ignore screening/non-screening when doing a full
analysis of your data. IMHO, anyway.
> Oh, it is a population based survey.
Then you ought to be looking at using your sampling weights and
design effects in PROC SURVEYREG or PROC SURVEYLOGISTIC if at all
David Cassell, CSC
Senior computing specialist