Yang Jing, Peng Limin
Department of Biostatistics and Bioinformatics, Emory University, Atlanta, Georgia, U.S.A.
Department of Biostatistics and Bioinformatics, Emory University, Atlanta, Georgia, U.S.A..
Biometrics. 2016 Sep;72(3):770-9. doi: 10.1111/biom.12491. Epub 2016 Feb 24.
Semi-competing risks data are often encountered in chronic disease follow-up studies that record both nonterminal events (e.g., disease landmark events) and terminal events (e.g., death). Studying the relationship between the nonterminal event and the terminal event can provide insightful information on disease progression. In this article, we propose a new sensible dependence measure tailored to addressing such an interest. We develop a nonparametric estimator, which is general enough to handle both independent right censoring and left truncation. Our strategy of connecting the new dependence measure with quantile regression enables a natural extension to adjust for covariates with minor additional assumptions imposed. We establish the asymptotic properties of the proposed estimators and develop inferences accordingly. Simulation studies suggest good finite-sample performance of the proposed methods. Our proposals are illustrated via an application to Denmark diabetes registry data.
半竞争风险数据在慢性病随访研究中经常遇到，这些研究记录了非终末事件（如疾病标志性事件）和终末事件（如死亡）。研究非终末事件与终末事件之间的关系可以提供有关疾病进展的深刻见解。在本文中，我们提出了一种新的合理依赖度量，专门用于解决此类问题。我们开发了一种非参数估计器，它具有足够的通用性来处理独立右删失和左截断。我们将新的依赖度量与分位数回归相联系的策略能够在施加少量额外假设的情况下自然地扩展以调整协变量。我们建立了所提出估计器的渐近性质，并据此进行推断。模拟研究表明所提出方法具有良好的有限样本性能。我们通过应用丹麦糖尿病登记数据来说明我们的提议。
