Skip Navigation



Biostatistics Advance Access published online on April 11, 2007
Biostatistics, doi:10.1093/biostatistics/kxm008
This Article
Right arrow Full Text
Right arrow Full Text (PDF)
Right arrow Supplementary Material
Right arrow All Versions of this Article:
8/4/821    most recent
kxm008v1
Right arrow Alert me when this article is cited
Right arrow Alert me if a correction is posted
Services
Right arrow Email this article to a friend
Right arrow Similar articles in this journal
Right arrow Similar articles in PubMed
Right arrow Alert me to new issues of the journal
Right arrow Add to My Personal Archive
Right arrow Download to citation manager
Right arrowRequest Permissions
Right arrow Disclaimer
Google Scholar
Right arrow Articles by Pennell, M. L.
Right arrow Articles by Dunson, D. B.
Right arrow Search for Related Content
PubMed
Right arrow PubMed Citation
Right arrow Articles by Pennell, M. L.
Right arrow Articles by Dunson, D. B.
Social Bookmarking
Add to CiteULike   Add to Connotea   Add to Del.icio.us  
What's this?

Published by Oxford University Press 2007.

Fitting semiparametric random effects models to large data sets

Michael L. Pennell*

Division of Biostatistics, College of Public Health, The Ohio State University, B-115 Starling-Loving Hall, 320 West 10th Avenue, Columbus, OH 43210, USA mpennell{at}cph.osu.edu

David B. Dunson

Biostatistics Branch, MD A3-03, National Institute of Environmental Health Sciences, PO Box 12233, Research Triangle Park, NC 27709, USA

* To whom correspondence should be addressed.

For large data sets, it can be difficult or impossible to fit models with random effects using standard algorithms due to memory limitations or high computational burdens. In addition, it would be advantageous to use the abundant information to relax assumptions, such as normality of random effects. Motivated by data from an epidemiologic study of childhood growth, we propose a 2-stage method for fitting semiparametric random effects models to longitudinal data with many subjects. In the first stage, we use a multivariate clustering method to identify G<<N groups of subjects whose data have no scientifically important differences, as defined by subject matter experts. Then, in stage 2, group-specific random effects are assumed to come from an unknown distribution, which is assigned a Dirichlet process prior, further clustering the groups from stage 1. We use our approach to model the effects of maternal smoking during pregnancy on growth in 17 518 girls.

Keywords: Cluster analysis; Dirichlet process; Latent variables; Longitudinal data; Mixed effects model; Prior elicitation

Received October 2, 2006; revised February 19, 2007; accepted for publication March 6, 2007.


Add to CiteULike CiteULike   Add to Connotea Connotea   Add to Del.icio.us Del.icio.us    What's this?




Disclaimer:
Please note that abstracts for content published before 1996 were created through digital scanning and may therefore not exactly replicate the text of the original print issues. All efforts have been made to ensure accuracy, but the Publisher will not be held responsible for any remaining inaccuracies. If you require any further clarification, please contact our Customer Services Department.