Jin Yuxue, Lai Tze Leung
Quantitative Marketing, Google, New York, NY, 10011, USA.
Department of Statistics, Stanford University, Stanford, CA, 94305, USA.
Lifetime Data Anal. 2017 Oct;23(4):605-625. doi: 10.1007/s10985-016-9378-8. Epub 2016 Aug 8.
An approximate likelihood approach is developed for regression analysis of censored competing-risks data. This approach models directly the cumulative incidence function, instead of the cause-specific hazard function, in terms of explanatory covariates under a proportional subdistribution hazards assumption. It uses a self-consistent iterative procedure to maximize an approximate semiparametric likelihood function, leading to an asymptotically normal and efficient estimator of the vector of regression parameters. Simulation studies demonstrate its advantages over previous methods.
本文提出了一种用于删失竞争风险数据回归分析的近似似然方法。该方法在比例子分布风险假设下，根据解释性协变量直接对累积发病率函数进行建模，而非病因特异性风险函数。它使用一种自洽迭代程序来最大化近似半参数似然函数，从而得到回归参数向量的渐近正态且有效的估计量。模拟研究证明了该方法相对于先前方法的优势。
