Work

Maximum Likelihood Estimation for Semiparametric Regression Models with Interval-Censored Multi-State Data

Year: 2022

Type: preprint

Abstract: Interval-censored multi-state data arise in many studies of chronic diseases, where the health status of a subject can be characterized by a finite number of disease states and the transition between any two states is only known to occur over a broad time interval. We formulate the effects of potentially time-dependent covariates on multi-state processes through semiparametric proportional intensity models with random effects. We adopt nonparametric maximum likelihood estimation (NPMLE) under general interval censoring and develop a stable expectation-maximization (EM) algorithm. We show that the resulting parameter estimators are consistent and that the finite-dimensional components are asymptotically normal with a covariance matrix that attains the semiparametric efficiency bound and can be consistently estimated through profile likelihood. In addition, we demonstrate through extensive simulation studies that the proposed numerical and inferential procedures perform well in realistic settings. Finally, we provide an application to a major epidemiologic cohort study.

Source: arXiv (Cornell University)

DOI: 10.48550/arxiv.2209.07708

Authors: Gu, Yu, Zeng, Donglin, Heiss, Gerardo, Lin, D. Y.

Institution: University of North Carolina at Chapel Hill

Language: English

Topic: Statistical Methods and Inference

Subfield: Statistics and Probability

Field: Mathematics

Domain: Physical Sciences

SDG: Good health and well-being

Open Access status: green

Funder: National Heart, Lung, and Blood Institute