Pan Yinghao, Cai Jianwen, Kim Sangmi, Zhou Haibo
Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599, U.S.A.
Medical College of Georgia, GRU Cancer Center, Augusta University, Augusta, Georgia 30912, U.S.A.
Biometrics. 2018 Sep;74(3):1014-1022. doi: 10.1111/biom.12838. Epub 2017 Dec 29.
Case-cohort study design has been widely used for its cost-effectiveness. In any real study, there are always other important outcomes of interest beside the failure time that the original case-cohort study is based on. How to utilize the available case-cohort data to study the relationship of a secondary outcome with the primary exposure obtained through the case-cohort study is not well studied. In this article, we propose a non-parametric estimated likelihood approach for analyzing a secondary outcome in a case-cohort study. The estimation is based on maximizing a semiparametric likelihood function that is built jointly on both time-to-failure outcome and the secondary outcome. The proposed estimator is shown to be consistent, efficient, and asymptotically normal. Finite sample performance is evaluated via simulation studies. Data from the Sister Study is analyzed to illustrate our method.
病例队列研究设计因其成本效益而被广泛使用。在任何实际研究中，除了原始病例队列研究所基于的失败时间外，总是存在其他重要的感兴趣的结局。如何利用现有的病例队列数据来研究次要结局与通过病例队列研究获得的主要暴露之间的关系，目前尚未得到充分研究。在本文中，我们提出了一种非参数估计似然方法，用于分析病例队列研究中的次要结局。该估计基于最大化一个半参数似然函数，该函数是基于失败时间结局和次要结局共同构建的。所提出的估计量被证明是一致的、有效的且渐近正态的。通过模拟研究评估有限样本性能。分析了姐妹研究的数据以说明我们的方法。
