Censored quantile regression with time-dependent covariates

Abstract We propose a new class of censored quantile regression models with time-dependent covariates for right-censored failure time data. While time-dependent covariates naturally arise in time-to-event analysis, existing works in the literature discuss treatments for data collected either under an independent censoring mechanism or a longitudinal setting. Our formulation extends the current scope so that the conventional setting of time-dependent covariates can be properly handled. The new framework also generalizes the definition of quantiles and offers a new dynamic perspective for interpretation. Asymptotic properties of the recursive estimator are established. Numerical studies are also presented to illustrate the effectiveness of our proposal.

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Publication Details

Journal
Journal of the Royal Statistical Society Series B (Statistical Methodology)
Published
2025-07-14
DOI
https://doi.org/10.1093/jrsssb/qkaf042
Primary Topic
Statistical Methods and Inference
Type
article
Field-Weighted Citation Impact
0.00

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article

Censored quantile regression with time-dependent covariates

Tony Sit, Chi Wing Chu, Zhiliang Ying
Journal of the Royal Statistical Society Series B (Statistical Methodology)
Statistical Methods and Inference
article

Censored quantile regression with time-dependent covariates

Tony Sit, Chi Wing Chu, Zhiliang Ying
article en

Abstract

Abstract We propose a new class of censored quantile regression models with time-dependent covariates for right-censored failure time data. While time-dependent covariates naturally arise in time-to-event analysis, existing works in the literature discuss treatments for data collected either under an independent censoring mechanism or a longitudinal setting. Our formulation extends the current scope so that the conventional setting of time-dependent covariates can be properly handled. The new framework also generalizes the definition of quantiles and offers a new dynamic perspective for interpretation. Asymptotic properties of the recursive estimator are established. Numerical studies are also presented to illustrate the effectiveness of our proposal.

Journal of the Royal Statistical Society Series B (Statistical Methodology)Vol. 88(1)
City University of Hong Kong (HK), Chinese University of Hong Kong (HK), Columbia University (US)
Research Grants Council, University Grants Committee
Peace, Justice and strong institutions, Reduced inequalities
Openalex Percentile: Top 80%
Statistical Methods and Inference
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