Semiparametric Regression Analysis of Interval-Censored Multi-State Data with An Absorbing State

• 成果类型: Article; Early Access
• 署名作者: Gu, Yu; Zeng, Donglin; Lin, D. Y.
• 署名单位: University of Hong Kong; University of Michigan System; University of Michigan; University of North Carolina; University of North Carolina Chapel Hill; University of North Carolina School of Medicine
• 刊物名称: JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
• ISSN/ISSBN: 0162-1459
• DOI: 10.1080/01621459.2024.2448858
• 发表日期: 2025
• 关键词: PROPORTIONAL HAZARDS MODEL; panel-data

摘要

In studies of chronic diseases, the health status of a subject can often be characterized by a finite number of transient disease states and an absorbing state, such as death. The times of transitions among the transient states are ascertained through periodic examinations and thus interval-censored. The time of reaching the absorbing state is known or right-censored, with the transient state at the previous instant being unobserved. In this article, we provide a general framework for analyzing such multi-state data. We formulate the effects of potentially time-dependent covariates on the multi-state disease process through semiparametric proportional intensity models with random effects. We combine nonparametric maximum likelihood estimation with sieve estimation and develop a stable expectation-maximization algorithm. We establish the asymptotic properties of the proposed estimators through novel use of modern empirical process theory, sieve estimation theory, and semiparametric efficiency theory. In addition, we dynamically predict future states and survival time using the evolving disease history. Finally, we assess the performance of the proposed methods through extensive simulation studies and provide an illustration with a cardiac allograft vasculopathy study. Supplementary materials for this article are available online, including a standardized description of the materials available for reproducing the work.