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Lifetime Data Anal. 2007 Dec;13(4):497-512. Epub 2007 Nov 14.

Generalized linear mixed models: a review and some extensions.

Author information

1
Department of Statistics and Actuarial Science, Simon Fraser University, Burnaby, BC, Canada V5A 1S6. dean@stat.sfu.ca

Abstract

Breslow and Clayton (J Am Stat Assoc 88:9-25,1993) was, and still is, a highly influential paper mobilizing the use of generalized linear mixed models in epidemiology and a wide variety of fields. An important aspect is the feasibility in implementation through the ready availability of related software in SAS (SAS Institute, PROC GLIMMIX, SAS Institute Inc., URL http://www.sas.com , 2007), S-plus (Insightful Corporation, S-PLUS 8, Insightful Corporation, Seattle, WA, URL http://www.insightful.com , 2007), and R (R Development Core Team, R: A Language and Environment for Statistical Computing, R Foundation for Statistical Computing, Vienna, Austria, URL http://www.R-project.org , 2006) for example, facilitating its broad usage. This paper reviews background to generalized linear mixed models and the inferential techniques which have been developed for them. To provide the reader with a flavor of the utility and wide applicability of this fundamental methodology we consider a few extensions including additive models, models for zero-heavy data, and models accommodating latent clusters.

PMID:
18000755
DOI:
10.1007/s10985-007-9065-x
[Indexed for MEDLINE]

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