A new approach for semi-parametric regression analysis of bivariate interval-censored outcomes from case-cohort studies

• Published in: Communications in statistics. Theory and methods Vol. 53; no. 15; pp. 5405 - 5420
• Main Authors: Lou, Yichen, Wang, Peijie, Sun, Jianguo
• Format: Journal Article
• Language: English
• Published: Philadelphia Taylor & Francis 02-08-2024; Taylor & Francis Ltd
• Subjects: Bivariate analysis; Bivariate interval-censored; case-cohort design; copula; Failure times; Maximum likelihood estimation; Regression analysis; weighted likelihood
• ISSN: 0361-0926; 1532-415X
• DOI: 10.1080/03610926.2023.2220850

Interval-censored failure time data frequently occur in many areas and a great deal of literature on their analyses has been established. In this article, we discuss the situation where one faces bivariate interval-censored data arising from case-cohort studies, which are commonly used as a tool to save costs when disease incidence is low and covariates are difficult to obtain. For this problem, a class of copula-based semi-parametric models is presented and for estimation, a sieve weighted maximum likelihood estimation procedure is developed. The resulting estimators of regression parameters are shown to be strongly consistent and asymptotically normal. Furthermore, the proposed method is generalized to the situation of non rare diseases. A simulation study is conducted to assess the finite sample performance of the proposed method and suggests that it performs well in practice.