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

Abstract

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.

KEYWORDS:

• Bivariate interval-censored
• case-cohort design
• copula
• weighted likelihood

Acknowledgments

The authors wish to thank the Editor in Chief, Prof. Balakrishnan, the Associate Editor, and two reviewers for their many helpful and useful comments and suggestions that greatly improved the paper.

Disclosure statement

The authors have declared no conflict of interest.

Data availability statement

The data that support the findings of this study are openly available in Kern et al. (2021) at https://doi.org/10.1038/s41433-020-1048-0.

Additional information

Funding

This work was partially supported by the Science and Technology Research Planning Project of Jilin Provincial Department of Education (Grant No. JJKH20231144KJ) and the Natural Science Foundation of Jilin Province (Grant No. 20230101002JC).