Skip Navigation


Biostatistics Advance Access originally published online on November 10, 2005
Biostatistics 2006 7(2):252-267; doi:10.1093/biostatistics/kxj005
This Article
Right arrow Full Text Freely available
Right arrow FREE Full Text (PDF) Freely available
Right arrow All Versions of this Article:
7/2/252    most recent
kxj005v1
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 Park, Y.
Right arrow Articles by Wei, L. J.
Right arrow Search for Related Content
PubMed
Right arrow PubMed Citation
Right arrow Articles by Park, Y.
Right arrow Articles by Wei, L. J.
Social Bookmarking
Add to CiteULike   Add to Connotea   Add to Del.icio.us  
What's this?

© The Author 2005. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oxfordjournals.org.

One- and two-sample nonparametric inference procedures in the presence of a mixture of independent and dependent censoring

Yuhyun Park

Department of Biostatistics, Harvard University, 677 Huntington Avenue, Boston, MA 02115, USA

Lu Tian

Department of Preventive Medicine, Northwestern University, 680 North Lake Shore Drive, Suite 1102, Chicago, IL 60611, USA

L. J. Wei*

Department of Biostatistics, Harvard University, 677 Huntington Avenue, Boston, MA 02115, USA wei{at}sdac.harvard.edu

* To whom correspondence should be addressed.

In survival analysis, the event time T is often subject to dependent censorship. Without assuming a parametric model between the failure and censoring times, the parameter {Theta} of interest, for example, the survival function of T, is generally not identifiable. On the other hand, the collection {Omega} of all attainable values for {Theta} may be well defined. In this article, we present nonparametric inference procedures for {Omega} in the presence of a mixture of dependent and independent censoring variables. By varying the criteria of classifying censoring to the dependent or independent category, our proposals can be quite useful for the so-called sensitivity analysis of censored failure times. The case that the failure time is subject to possibly dependent interval censorship is also discussed in this article. The new proposals are illustrated with data from two clinical studies on HIV-related diseases.

Keywords: Competing risks; Martingale; Sensitivity analysis; Simultaneous confidence interval; Survival analysis


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


This article has been cited by other articles:


Home page
 BiostatisticsHome page
J. Zhang and D. F. Heitjan
Impact of nonignorable coarsening on Bayesian inference
Biostat., October 1, 2007; 8(4): 722 - 743.
[Abstract] [Full Text] [PDF]

Home page
 NEJMHome page
S. W. Lagakos
Time-to-Event Analyses for Long-Term Treatments -- The APPROVe Trial
N. Engl. J. Med., July 13, 2006; 355(2): 113 - 117.
[Full Text] [PDF]


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.