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

ABSTRACT: Interval-censored multivariate failure time data arise when there are multiple types of failure or there is clustering of study subjects and each failure time is known only to lie in a certain interval. We investigate the effects of possibly time-dependent covariates on multivariate failure times by considering a broad class of semiparametric transformation models with random effects, and we study nonparametric maximum likelihood estimation under general interval-censoring schemes. We show that the proposed estimators for the finite-dimensional parameters are consistent and asymptotically normal, with a limiting covariance matrix that attains the semiparametric efficiency bound and can be consistently estimated through profile likelihood. In addition, we develop an EM algorithm that conver

SUBMITTER: Zeng D 

PROVIDER: S-EPMC5787874 | biostudies-literature | 2017 Sep

REPOSITORIES: biostudies-literature