Semiparametric regression analysis of interval-censored data with informative dropout.

TitleSemiparametric regression analysis of interval-censored data with informative dropout.
Publication TypeJournal Article
Year of Publication2018
AuthorsGao, Fei, Donglin Zeng, and Dan-Yu Lin
JournalBiometrics
Volume74
Issue4
Pagination1213-1222
Date Published2018 12
ISSN1541-0420
KeywordsAlgorithms, Biometry, Computer Simulation, Diabetes Mellitus, Type 1, Humans, Incidence, Likelihood Functions, Models, Statistical, Regression Analysis
Abstract

Interval-censored data arise when the event time of interest can only be ascertained through periodic examinations. In medical studies, subjects may not complete the examination schedule for reasons related to the event of interest. In this article, we develop a semiparametric approach to adjust for such informative dropout in regression analysis of interval-censored data. Specifically, we propose a broad class of joint models, under which the event time of interest follows a transformation model with a random effect and the dropout time follows a different transformation model but with the same random effect. We consider nonparametric maximum likelihood estimation and develop an EM algorithm that involves simple and stable calculations. We prove that the resulting estimators of the regression parameters are consistent, asymptotically normal, and asymptotically efficient with a covariance matrix that can be consistently estimated through profile likelihood. In addition, we show how to consistently estimate the survival function when dropout represents voluntary withdrawal and the cumulative incidence function when dropout is an unavoidable terminal event. Furthermore, we assess the performance of the proposed numerical and inferential procedures through extensive simulation studies. Finally, we provide an application to data on the incidence of diabetes from a major epidemiological cohort study.

DOI10.1111/biom.12911
Alternate JournalBiometrics
Original PublicationSemiparametric regression analysis of interval-censored data with informative dropout.
PubMed ID29870067
PubMed Central IDPMC6309250
Grant ListR01 CA082659 / CA / NCI NIH HHS / United States
HHSN268201100012C / HL / NHLBI NIH HHS / United States
HHSN268201100009I / HL / NHLBI NIH HHS / United States
HHSN268201100010C / HL / NHLBI NIH HHS / United States
HHSN268201100008C / HL / NHLBI NIH HHS / United States
HHSN268201100005G / HL / NHLBI NIH HHS / United States
HHSN268201100008I / HL / NHLBI NIH HHS / United States
HHSN268201100007C / HL / NHLBI NIH HHS / United States
HHSN268201100011I / HL / NHLBI NIH HHS / United States
HHSN268201100011C / HL / NHLBI NIH HHS / United States
R01 AI029168 / AI / NIAID NIH HHS / United States
R01 GM047845 / GM / NIGMS NIH HHS / United States
HHSN268201100006C / HL / NHLBI NIH HHS / United States
HHSN268201100005I / HL / NHLBI NIH HHS / United States
HHSN268201100009C / HL / NHLBI NIH HHS / United States
HHSN268201100005C / HL / NHLBI NIH HHS / United States
HHSN268201100007I / HL / NHLBI NIH HHS / United States
P01 CA142538 / CA / NCI NIH HHS / United States
Project: