Hsieh Jin-Jian, Huang Yu-Ting
Department of Mathematics, National Chung Cheng University, Chia-Yi, Taiwan, R.O.C.
Lifetime Data Anal. 2012 Jul;18(3):302-20. doi: 10.1007/s10985-012-9219-3. Epub 2012 Mar 11.
Medical studies often involve semi-competing risks data, which consist of two types of events, namely terminal event and non-terminal event. Because the non-terminal event may be dependently censored by the terminal event, it is not possible to make inference on the non-terminal event without extra assumptions. Therefore, this study assumes that the dependence structure on the non-terminal event and the terminal event follows a copula model, and lets the marginal regression models of the non-terminal event and the terminal event both follow time-varying effect models. This study uses a conditional likelihood approach to estimate the time-varying coefficient of the non-terminal event, and proves the large sample properties of the proposed estimator. Simulation studies show that the proposed estimator performs well. This study also uses the proposed method to analyze AIDS Clinical Trial Group (ACTG 320).
医学研究经常涉及半竞争风险数据，其由两种类型的事件组成，即终末事件和非终末事件。由于非终末事件可能会被终末事件依赖性删失，因此在没有额外假设的情况下，无法对非终末事件进行推断。因此，本研究假设非终末事件和终末事件的相依结构遵循一个Copula模型，并让非终末事件和终末事件的边际回归模型都遵循时变效应模型。本研究使用条件似然方法来估计非终末事件的时变系数，并证明了所提出估计量的大样本性质。模拟研究表明，所提出的估计量表现良好。本研究还使用所提出的方法对艾滋病临床试验组（ACTG 320）进行了分析。
