The consequences of misspecifying the random-effects distribution when fitting generalized linear mixed models
AbstractGeneralized linear mixed models provide effective analyses of clustered and longitudinal data and typically require the specification of the distribution of the random effects. The consequences of misspecifying this distribution are subject to debate; some authors suggest that large biases can arise, while others show that there will typically be little bias for the parameters of interest. Using analytic results, simulation studies, and example data, I summarize the results of extensive assessments of the bias in parameter estimates due to random-effects distribution misspecification. I also present assessments of the accuracy of random-effects predictions under misspecification. These assessments indicate that random-effects distribution misspecification often produces little bias when estimating slope coefficients but may yield biased intercepts and variance-components estimators as well as mildly inaccurate predicted random effects.
Download InfoIf you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.
Bibliographic InfoPaper provided by Stata Users Group in its series Fall North American Stata Users' Group Meetings 2008 with number 19.
Date of creation: 16 Nov 2008
Date of revision:
This paper has been announced in the following NEP Reports:
- NEP-ALL-2008-11-25 (All new papers)
You can help add them by filling out this form.
reading list or among the top items on IDEAS.Access and download statistics
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Christopher F Baum).
If references are entirely missing, you can add them using this form.