Estimating cross quantile residual ratio with left-truncated semi-competing risks data

Jing Yang () and Limin Peng ()
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Jing Yang: Emory University
Limin Peng: Emory University

Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, 2018, vol. 24, issue 4, No 10, 652-674

Abstract: Abstract A semi-competing risks setting often arises in biomedical studies, involving both a nonterminal event and a terminal event. Cross quantile residual ratio (Yang and Peng in Biometrics 72:770–779, 2016) offers a flexible and robust perspective to study the dependency between the nonterminal and the terminal events which can shed useful scientific insight. In this paper, we propose a new nonparametric estimator of this dependence measure with left truncated semi-competing risks data. The new estimator overcomes the limitation of the existing estimator that is resulted from demanding a strong assumption on the truncation mechanism. We establish the asymptotic properties of the proposed estimator and develop inference procedures accordingly. Simulation studies suggest good finite-sample performance of the proposed method. Our proposal is illustrated via an application to Denmark diabetes registry data.

Keywords: Estimating equation; Left truncation; Quantile residual time; Semi-competing risks (search for similar items in EconPapers)
Date: 2018
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DOI: 10.1007/s10985-017-9412-5

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