Message-ID: <Pine.A41.4.61b.0505261305140.303294@homer12.u.washington.edu>
Date: 2005-05-26T20:11:52Z
From: Thomas Lumley
Subject: longitudinal survey data
In-Reply-To: <1117131639.429613772e707@webmail.utoronto.ca>
On Thu, 26 May 2005 h.brunschwig at utoronto.ca wrote:
>
> Dear R-Users!
>
> Is there a possibility in R to do analyze longitudinal survey data (repeated
> measures in a survey)? I know that for longitudinal data I can use lme() to
> incorporate the correlation structure within individual and I know that there is
> the package survey for analyzing survey data. How can I combine both? I am
> trying to calculate design-based estimates. However, if I use svyglm() from the
> survey package I would ignore the correlation structure of the repeated measures.
>
You *can* fit regression models to these data with svyglm(). Remember that
from a design-based point of view there is no such thing as a correlation
structure of repeated measures -- only the sampling is random, not the
population data.
If you *want* to fit mixed models (eg because you are interested in
estimating variance components, or perhaps to gain efficiency) then it's
quite a bit trickier. You can't just use the sampling weights in lme().
You can correct for the biased sampling if you put the variables that
affect the weights in as predictors in the model. Cluster sampling could
perhaps then be modelled as another level of random effect.
-thomas
Thomas Lumley Assoc. Professor, Biostatistics
tlumley at u.washington.edu University of Washington, Seattle