A series of two-sample non-parametric tests for quantile residual life time

Yimeng Liu (), Liwen Wu, Gong Tang and Abdus S. Wahed
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Yimeng Liu: University of Pittsburgh
Liwen Wu: University of Pittsburgh
Gong Tang: University of Pittsburgh
Abdus S. Wahed: University of Pittsburgh

Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, 2023, vol. 29, issue 1, No 9, 234-252

Abstract: Abstract Quantile residual lifetime (QRL) is of significant interest in many clinical studies as an easily interpretable quantity compared to other summary measures of survival distributions. In cancer or other chronic diseases, treatments are often compared based on the distributions or quantiles of the residual lifetime. Thus a common problem of interest is to test the equality of the QRL between two populations. In this paper, we propose two classes of tests to compare two QRLs; one class is based on the difference between two estimated QRLs, and the other is based on the estimating function of the QRL, where the estimated QRL from one sample is plugged into the QRL-estimating-function of the other sample. We outline the asymptotic properties of these test statistics. Simulation studies demonstrate that the proposed tests produced Type I errors closer to the nominal level and are superior to some existing tests based on both Type I error and power. Our proposed test statistics are also computationally less intensive and more straightforward compared to tests based on the confidence intervals. We applied the proposed methods to a randomized multicenter phase III trial for breast cancer patients.

Keywords: Inverse probability weighting; Kaplan–Meier; Kernel density estimators; Quantile residual lifetime; Residual lifetime; Wald test (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1007/s10985-022-09580-6

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