Maximum likelihood estimation for semiparametric regression models with interval-censored multistate data.

ABSTRACT: Interval-censored multistate 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 relate potentially time-dependent covariates to multistate processes through semiparametric proportional intensity models with random effects. We study nonparametric maximum likelihood estimation under general interval censoring and develop a stable expectation-maximization 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 throug

SUBMITTER: Gu Y 

PROVIDER: S-EPMC11373756 | biostudies-literature | 2024 Sep

REPOSITORIES: biostudies-literature