Eliminating bias due to censoring in Kendall's tau estimators for quasi-independence of truncation and failure

Matthew D. Austin, Rebecca A. Betensky

Research output: Contribution to journalArticle

Abstract

While the currently available estimators for the conditional Kendall's tau measure of association between truncation and failure are valid for testing the null hypothesis of quasi-independence, they are biased when the null does not hold. This is because they converge to quantities that depend on the censoring distribution. The magnitude of the bias relative to the theoretical Kendall's tau measure of association between truncation and failure due to censoring has not been studied, and so its importance in real problems is not known. We quantify this bias in order to assess the practical usefulness of the estimators. Furthermore, we propose inverse probability weighted versions of the conditional Kendall's tau estimators to remove the effects of censoring and provide asymptotic results for the estimators. In simulations, we demonstrate the decrease in bias achieved by these inverse probability weighted estimators. We apply the estimators to the Channing House data set and an AIDS incubation data set.

Original languageEnglish (US)
Pages (from-to)16-26
Number of pages11
JournalComputational Statistics and Data Analysis
Volume73
DOIs
StatePublished - May 2014

Keywords

  • Inverse probability weighting
  • Left truncation

ASJC Scopus subject areas

  • Statistics and Probability
  • Computational Mathematics
  • Computational Theory and Mathematics
  • Applied Mathematics

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