所属单位：数学与统计学院
教研室：统计教研室
发表刊物：Statistical Methods in Medical Research
项目来源：国家自然科学基金项目
关键字：Bernstein polynomial;Event history study;Frailty model;Sieve maximum likelihood estimation
摘要：Interval-censored failure time and panel count data, which frequently arise in medical studies and social sciences, are two types of important incomplete data. Although methods for their joint analysis have been available in the literature, they did not consider the observation process, which may depend on the failure time and/or panel count of interest. This study considers a three-component joint model to analyze interval-censored failure time, panel counts, and the observation process within a unique framework. Gamma and distribution-free frailties are introduced to jointly model the interdependency among the interval-censored data, panel count data, and the observation process. We propose a sieve maximum likelihood approach coupled with Bernstein polynomial approximation to estimate the unknown parameters and baseline hazard function. The asymptotic properties of the resulting estimators are established. An extensive simulation study suggests that the proposed procedure works well for practical situations. An application of the method to a real-life dataset collected from a cardiac allograft vasculopathy study is presented.
论文类型：期刊论文
卷号：31
期号：11
页面范围：2054-2068
是否译文：否
发表时间：2022-07-12
收录刊物：SCI