Case-cohort studies with interval-censored failure time data
针对失效时间仅知落在某个随机区间内的区间删失数据，提出了病例队列设计下的筛半参数似然方法，用逆概率加权和伯恩斯坦多项式构建似然函数，证明了估计量的相合性和渐近正态性，并通过模拟和实例验证了方法的有效性。
The case-cohort design has been widely used as a means of cost reduction in assembling or measuring expensive covariates in large cohort studies. The existing literature on the case-cohort design is mainly focused on right-censored data. In practice, however, the failure time is often subject to interval-censoring; it is known only to fall within some random time interval. In this paper, we consider the case-cohort study design for interval-censored failure time and develop a sieve semiparametric likelihood approach for analyzing data from this design under the proportional hazards model. We construct the likelihood function using inverse probability weighting and build the sieves with Bernstein polynomials. The consistency and asymptotic normality of the resulting regression parameter estimator are established and a weighted bootstrap procedure is considered for variance estimation. Simulations show that the proposed method works well for practical situations, and an application to real data is provided.