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

机译:使用左截断的半竞争风险数据估计交叉分位数残差比率

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摘要

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.
机译:在生物医学研究中,经常出现半竞争性风险设置,涉及非终末事件和终末事件。交叉分位数残差比率(Yang和Peng,Biometrics 72:770-779,2016)为研究非终结事件和终结事件之间的依赖性提供了灵活而稳健的观点,这可以提供有用的科学见解。在本文中,我们提出了一种具有左截断的半竞争风险数据的依赖度量的新非参数估计器。新的估计器克服了现有估计器的局限性,后者是由于要求对截断机制进行严格假设而导致的。我们建立了所提出估计量的渐近性质,并据此开发了推理程序。仿真研究表明,该方法具有良好的有限样本性能。通过对丹麦糖尿病登记数据的应用说明了我们的建议。

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