Skip Navigation


Biostatistics Advance Access originally published online on March 18, 2009
Biostatistics 2009 10(3):451-467; doi:10.1093/biostatistics/kxp004
This Article
Right arrow Full Text
Right arrow Full Text (PDF)
Right arrow Supplementary Materials
Right arrow All Versions of this Article:
10/3/451    most recent
kxp004v1
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 Clement, D. Y.
Right arrow Articles by Strawderman, R. L.
PubMed
Right arrow PubMed Citation
Right arrow Articles by Clement, D. Y.
Right arrow Articles by Strawderman, R. L.
Social Bookmarking
Add to CiteULike   Add to Connotea   Add to Del.icio.us  
What's this?

© The Author 2009. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oxfordjournals.org.

Conditional GEE for recurrent event gap times

David Y. Clement

Department of Statistical Science, Cornell University, Ithaca, NY 14853-7801, USA
dyc24{at}cornell.edu

Robert L. Strawderman*

Department of Biological Statistics and Computational Biology and Department of Statistical Science, Cornell University, Ithaca, NY 14853-7801, USA
rls54{at}cornell.edu

* To whom correspondence should be addressed.

This paper deals with the analysis of recurrent event data subject to censored observation. Using a suitable adaptation of generalized estimating equations for longitudinal data, we propose a straightforward methodology for estimating the parameters indexing the conditional means and variances of the process interevent (i.e. gap) times. The proposed methodology permits the use of both time-fixed and time-varying covariates, as well as transformations of the gap times, creating a flexible and useful class of methods for analyzing gap-time data. Censoring is dealt with by imposing a parametric assumption on the censored gap times, and extensive simulation results demonstrate the relative robustness of parameter estimates even when this parametric assumption is incorrect. A suitable large-sample theory is developed. Finally, we use our methods to analyze data from a randomized trial of asthma prevention in young children.

Keywords: Asthma; Censoring; Generalized estimating equation; Intensity model; Longitudinal data; Marginal model

Received February 21, 2008; revised October 16, 2008; accepted for publication November 18, 2008.


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.