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Stat Med. 2005 Oct 30;24(20):3089-110.

Adjusted Kaplan-Meier estimator and log-rank test with inverse probability of treatment weighting for survival data.

Author information

  • 1Department of Statistics, Purdue University, 150 N. University Street, West Lafayette, IN 47907-2067, USA. junxie@stat.purdue.edu

Erratum in

  • Stat Med. 2007 May 10;26(10):2276.

Abstract

Estimation and group comparison of survival curves are two very common issues in survival analysis. In practice, the Kaplan-Meier estimates of survival functions may be biased due to unbalanced distribution of confounders. Here we develop an adjusted Kaplan-Meier estimator (AKME) to reduce confounding effects using inverse probability of treatment weighting (IPTW). Each observation is weighted by its inverse probability of being in a certain group. The AKME is shown to be a consistent estimate of the survival function, and the variance of the AKME is derived. A weighted log-rank test is proposed for comparing group differences of survival functions. Simulation studies are used to illustrate the performance of AKME and the weighted log-rank test. The method proposed here outperforms the Kaplan-Meier estimate, and it does better than or as well as other estimators based on stratification. The AKME and the weighted log-rank test are applied to two real examples: one is the study of times to reinfection of sexually transmitted diseases, and the other is the primary biliary cirrhosis (PBC) study.

2005 John Wiley & Sons, Ltd.

PMID:
16189810
[PubMed - indexed for MEDLINE]
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