Estimation of the marginal survival time in the presence of dependent competing risks using inverse probability of censoring weighted (IPCW) methods

Yutaka Matsuyama, Takuhiro Yamaguchi

Research output: Contribution to journalArticlepeer-review

14 Citations (Scopus)

Abstract

In medical studies, there is interest in inferring the marginal distribution of a survival time subject to competing risks. The Kyushu Lipid Intervention Study (KLIS) was a clinical study for hypercholesterolemia, where pravastatin treatment was compared with conventional treatment. The primary endpoint was time to events of coronary heart disease (CHD). In this study, however, some subjects died from causes other than CHD or were censored due to loss to follow-up. Because the treatments were targeted to reduce CHD events, the investigators were interested in the effect of the treatment on CHD events in the absence of causes of death or events other than CHD. In this paper, we present a method for estimating treatment group-specific marginal survival curves of time-to-event data in the presence of dependent competing risks. The proposed method is a straightforward extension of the Inverse Probability of Censoring Weighted (IPCW) method to settings with more than one reason for censoring. The results of our analysis showed that the IPCW marginal incidence for CHD was almost the same as the lower bound for which subjects with competing events were assumed to be censored at the end of all follow-up. This result provided reassurance that the results in KLIS were robust to competing risks.

Original languageEnglish
Pages (from-to)202-214
Number of pages13
JournalPharmaceutical Statistics
Volume7
Issue number3
DOIs
Publication statusPublished - 2008 Jul 1
Externally publishedYes

Keywords

  • Cause of failure
  • Competing risks
  • Cox proportional hazards model
  • Inverse weighting methods
  • Kaplan - Meier estimator
  • Survival analysis

ASJC Scopus subject areas

  • Statistics and Probability
  • Pharmacology
  • Pharmacology (medical)

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