Improving the efficiency of the log-rank test using auxiliary covariates
Under the assumption of proportional hazards, the log-rank test is optimal for testing the null hypothesis <inline-formula><inline-graphic xlink:href="asn003ilm1.gif" xmlns:xlink="http://www.w3.org/1999/xlink"/></inline-formula>, where <inline-formula><inline-graphic xlink:href="asn003ilm2.gif" xmlns:xlink="http://www.w3.org/1999/xlink"/></inline-formula> denotes the logarithm of the hazard ratio. However, if there are additional covariates that correlate with survival times, making use of their information will increase the efficiency of the log-rank test. We apply the theory of semiparametrics to characterize a class of regular and asymptotically linear estimators for <inline-formula><inline-graphic xlink:href="asn003ilm3.gif" xmlns:xlink="http://www.w3.org/1999/xlink"/></inline-formula> when auxiliary covariates are incorporated into the model, and derive estimators that are more efficient. The Wald tests induced by these estimators are shown to be more powerful than the log-rank test. Simulation studies are used to illustrate the gains in efficiency. Copyright 2008, Oxford University Press.
Year of publication: |
2008
|
---|---|
Authors: | Lu, Xiaomin ; Tsiatis, Anastasios A. |
Published in: |
Biometrika. - Biometrika Trust, ISSN 0006-3444. - Vol. 95.2008, 3, p. 679-694
|
Publisher: |
Biometrika Trust |
Saved in:
Online Resource
Saved in favorites
Similar items by person
-
Rank-Based Analyses of Stratified Experiments: Alternatives to the van Elteren Test
Mehrotra, Devan V., (2010)
-
Murray, Susan, (2001)
-
Sequential Methods for Comparing Years of Life Saved in the Two-Sample Censored Data Problem
Murray, Susan, (1999)
- More ...