TY - JOUR

T1 - Eliminating bias due to censoring in Kendall's tau estimators for quasi-independence of truncation and failure

AU - Austin, Matthew D.

AU - Betensky, Rebecca A.

N1 - Funding Information:
This research was supported in part by NIH grants T32NS048005 and R01CA075971 .
Copyright:
Copyright 2014 Elsevier B.V., All rights reserved.

PY - 2014/5

Y1 - 2014/5

N2 - 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.

AB - 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.

KW - Inverse probability weighting

KW - Left truncation

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U2 - 10.1016/j.csda.2013.11.018

DO - 10.1016/j.csda.2013.11.018

M3 - Article

AN - SCOPUS:84890211810

VL - 73

SP - 16

EP - 26

JO - Computational Statistics and Data Analysis

JF - Computational Statistics and Data Analysis

SN - 0167-9473

ER -