Case-cohort studies with interval-censored failure time data

• 成果类型: Article
• 署名作者: Zhou, Q.; Zhou, H.; Cai, J.
• 署名单位: University of North Carolina; University of North Carolina Chapel Hill; University of North Carolina School of Medicine
• 刊物名称: BIOMETRIKA
• ISSN/ISSBN: 0006-3444
• DOI: 10.1093/biomet/asw067
• 发表日期: 2017
• 页码: 1729
• 关键词: PROPORTIONAL HAZARDS MODEL; maximum-likelihood-estimation; semiparametric transformation models; dependent sampling scheme; Goodness-of-fit; cox model; efficient estimation; REGRESSION-MODEL; truncated data; CONVERGENCE

摘要

The case-cohort design has been widely used as a means of cost reduction in collecting 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 to fall only 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 method for analysing 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 in practical situations, and an application to real data is provided.