Abstract

The quantile residual lifetime (QRL) regression is an attractive tool for assessing

covariate effects on the distribution of residual life expectancy, which is often of interest

in clinical studies. When study subjects may experience multiple events of interest, the

resulting failure times for the same subject are likely to be correlated.

To accommodate such correlation in assessing the covariate effects on QRL, we propose a marginal

semiparametric QRL regression model for multivariate failure time data. Our proposal

facilitates parameter estimation using unbiased estimating equations, yielding estimators

that are consistent and asymptotically normal. To address additional challenges in inference, we develop three approaches for variance estimation based on resampling techniques

and a sandwich estimator, and further construct a Wald-type test statistic for hypothesis testing. The simulation studies and an application to real data offer evidence of the

satisfactory performance and practical utility of the proposed method.

Information

Preprint No.SS-2024-0369
Manuscript IDSS-2024-0369
Complete AuthorsTonghui Yu, Liming Xiang, Jong-Hyeon Jeong
Corresponding AuthorsLiming Xiang
Emailslmxiang@ntu.edu.sg

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Acknowledgments

We are grateful to the editor, the associate editor, and three referees for their

valuable comments and suggestions, which have greatly improved the article. This

research was supported by the Singapore Ministry of Education Academic Research

Fund Tier 2 Grant (MOE-T2EP20121-0004).

Supplementary Materials

The online Supplementary Material contains an appendix for technical proofs

of the lemma and theorems referenced in Section 3 and additional numerical results

referenced in Sections 4-5.

Supplementary materials are available for download.