Li Chenxi
Department of Epidemiology and Biostatistics, Michigan State University, East Lansing, MI 48824, U.S.A.
Comput Stat Data Anal. 2016 Dec;104:197-208. doi: 10.1016/j.csda.2016.07.003. Epub 2016 Jul 14.
Inference for cause-specific hazards from competing risks data under interval censoring and possible left truncation has been understudied. Aiming at this target, a penalized likelihood approach for a Cox-type proportional cause-specific hazards model is developed, and the associated asymptotic theory is discussed. Monte Carlo simulations show that the approach performs very well for moderate sample sizes. An application to a longitudinal study of dementia illustrates the practical utility of the method. In the application, the age-specific hazards of AD, other dementia and death without dementia are estimated, and risk factors of all competing risks are studied.
在区间删失和可能的左截断情况下，基于竞争风险数据对特定病因风险进行推断的研究较少。针对这一目标，开发了一种用于Cox型比例特定病因风险模型的惩罚似然方法，并讨论了相关的渐近理论。蒙特卡罗模拟表明，该方法在中等样本量时表现良好。一项针对痴呆症的纵向研究的应用说明了该方法的实际效用。在该应用中，估计了阿尔茨海默病、其他痴呆症以及无痴呆症死亡的年龄特异性风险，并研究了所有竞争风险的危险因素。
