Skip Navigation



Biostatistics Advance Access published online on March 18, 2008
Biostatistics, doi:10.1093/biostatistics/kxm059
This Article
Right arrow Full Text
Right arrow Full Text (PDF)
Right arrow Supplementary Material
Right arrow All Versions of this Article:
9/4/686    most recent
kxm059v1
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 Berhane, K.
Right arrow Articles by Molitor, N.-T.
Right arrow Search for Related Content
PubMed
Right arrow PubMed Citation
Right arrow Articles by Berhane, K.
Right arrow Articles by Molitor, N.-T.
Social Bookmarking
Add to CiteULike   Add to Connotea   Add to Del.icio.us  
What's this?

© The Author 2008. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oxfordjournals.org.

A Bayesian approach to functional-based multilevel modeling of longitudinal data: applications to environmental epidemiology

Kiros Berhane*

Department of Preventive Medicine, University of Southern California, Los Angeles, CA 90033-9987, USA
kiros{at}usc.edu

Nuoo-Ting Molitor

Department of Epidemiology and Public Health, Imperial College London, Norfolk Place, London W2 1PG, UK
jassy.molitor{at}imperial.ac.uk

* To whom correspondence should be addressed.

Flexible multilevel models are proposed to allow for cluster-specific smooth estimation of growth curves in a mixed-effects modeling format that includes subject-specific random effects on the growth parameters. Attention is then focused on models that examine between-cluster comparisons of the effects of an ecologic covariate of interest (e.g. air pollution) on nonlinear functionals of growth curves (e.g. maximum rate of growth). A Gibbs sampling approach is used to get posterior mean estimates of nonlinear functionals along with their uncertainty estimates. A second-stage ecologic random-effects model is used to examine the association between a covariate of interest (e.g. air pollution) and the nonlinear functionals. A unified estimation procedure is presented along with its computational and theoretical details. The models are motivated by, and illustrated with, lung function and air pollution data from the Southern California Children's Health Study.

Keywords: Air pollution; Correlated data; Growth curves; Mixed-effects; Splines

Received January 31, 2007; revised November 7, 2007; accepted for publication December 17, 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.