Survival analysis using auxiliary variables via non-parametric multiple imputation

Chiu Hsieh Hsu, Jeremy M.G. Taylor, Susan Murray, Daniel Commenges

Research output: Contribution to journalArticle

39 Scopus citations

Abstract

We develop an approach, based on multiple imputation, that estimates the marginal survival distribution in survival analysis using auxiliary variables to recover information for censored observations. To conduct the imputation, we use two working survival models to define a nearest neighbour imputing risk set. One model is for the event times and the other for the censoring times. Based on the imputing risk set, two non-parametric multiple imputation methods are considered: risk set imputation, and Kaplan-Meier imputation. For both methods a future event or censoring time is imputed for each censored observation. With a categorical auxiliary variable, we show that with a large number of imputes the estimates from the Kaplan-Meier imputation method correspond to the weighted Kaplan-Meier estimator. We also show that the Kaplan-Meier imputation method is robust to mis-specification of either one of the two working models. In a simulation study with time independent and time-dependent auxiliary variables, we compare the multiple imputation approaches with an inverse probability of censoring weighted method. We show that all approaches can reduce bias due to dependent censoring and improve the efficiency. We apply the approaches to AIDS clinical trial data comparing ZDV and placebo, in which CD4 count is the time-dependent auxiliary variable.

Original languageEnglish (US)
Pages (from-to)3503-3517
Number of pages15
JournalStatistics in Medicine
Volume25
Issue number20
DOIs
StatePublished - Oct 30 2006

Keywords

  • Double robustness
  • Multiple imputation
  • Nearest neighbour

ASJC Scopus subject areas

  • Epidemiology
  • Statistics and Probability

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