Stochastic Functional Estimates in Longitudinal Models with Interval-Censored Anchoring Events.

ABSTRACT: Timelines of longitudinal studies are often anchored by specific events. In the absence of fully observed the anchoring event times, the study timeline becomes undefined, and the traditional longitudinal analysis loses its temporal reference. In this paper, we considered an analytical situation where the anchoring events are interval-censored. We demonstrated that by expressing the regression parameter estimators as stochastic functionals of a plug-in estimate of the unknown anchoring event time distribution, the standard longitudinal models could be extended to accommodate the situation of less well-defined timelines. We showed that for a broad class of longitudinal models, the functional parameter estimates are consistent and asymptotically normally distributed with a n convergence ra

SUBMITTER: Chu C 

PROVIDER: S-EPMC8315311 | biostudies-literature | 2020 Sep

REPOSITORIES: biostudies-literature