Skip Navigation

Biometrika 2001 88(2):421-434; doi:10.1093/biomet/88.2.421
© 2001 by Biometrika Trust
This Article
Right arrow Full Text (PDF)
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 Alert me to new issues of the journal
Right arrow Add to My Personal Archive
Right arrow Download to citation manager
Right arrowRequest Permissions
Google Scholar
Right arrow Articles by DiRienzo, A. G.
Right arrow Articles by Lagakos, S. W.
Right arrow Search for Related Content
Social Bookmarking
 Add to CiteULike   Add to Connotea   Add to Del.icio.us  
What's this?

Bias correction for score tests arising from misspecified proportional hazards regression models

A.G. DiRienzo1 and S.W. Lagakos1

1 Department of Biostatistics, Harvard School of Public Health, Boston, Massachusetts 02115, U.S.Adirienzo{at}biostat.harvard.edu lagakos{at}biostat.harvard.edu

When censoring depends on both treatment group and covariates, tests for a randomised treatment effect arising from Cox's proportional hazards model are not in general centred at zero under the null and can be seriously distorted.We propose a corrected test that modifies the at-risk indicator in a way that corrects for this bias. The resulting test is asymptotically valid regardless of whether or not the fitted model is correctly specified. Implementation of the corrected test requires that the dependence of censoring on treatment group and covariates be modelled. Simulations indicate that the bias-corrected test performs well, even when the dependence of censoring on treatment group and covariates is not modelled exactly, and maintains high efficiency relative to the uncorrected test when the latter is valid.

Key Words: Bias correction; Cox proportional hazards model; Misspecified model; Partial likelihood; Relative efficiency; Weighted log-rank test


Received April 1999. Revised October 2000


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
Y. Park, L. Tian, and L. J. Wei
One- and two-sample nonparametric inference procedures in the presence of a mixture of independent and dependent censoring
Biostat., April 1, 2006; 7(2): 252 - 267.
[Abstract] [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.