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Semiparametric Regression Analysis of Multiple Right- and Interval-Censored Events.

TitleSemiparametric Regression Analysis of Multiple Right- and Interval-Censored Events.
Publication TypeJournal Article
Year of Publication2019
AuthorsGao F, Zeng D, Couper DJ, Lin DY
JournalJ Am Stat Assoc
Volume114
Issue527
Pagination1232-1240
Date Published2019
ISSN0162-1459
Abstract

Health sciences research often involves both right- and interval-censored events because the occurrence of a symptomatic disease can only be observed up to the end of follow-up, while the occurrence of an asymptomatic disease can only be detected through periodic examinations. We formulate the effects of potentially time-dependent covariates on the joint distribution of multiple right- and interval-censored events through semiparametric proportional hazards models with random effects that capture the dependence both within and between the two types of events. We consider nonparametric maximum likelihood estimation and develop a simple and stable EM algorithm for computation. We show that the resulting estimators are consistent and the parametric components are asymptotically normal and efficient with a covariance matrix that can be consistently estimated by profile likelihood or nonparametric bootstrap. In addition, we leverage the joint modelling to provide dynamic prediction of disease incidence based on the evolving event history. Furthermore, we assess the performance of the proposed methods through extensive simulation studies. Finally, we provide an application to a major epidemiological cohort study. Supplementary materials for this article are available online.

DOI10.1080/01621459.2018.1482756
Alternate JournalJ Am Stat Assoc
PubMed ID31588157
PubMed Central IDPMC6777710
Grant ListR01 CA082659 / CA / NCI NIH HHS / United States
R37 AI029168 / AI / NIAID NIH HHS / United States
R01 GM124104 / GM / NIGMS NIH HHS / United States
HHSN268201700001I / HL / NHLBI NIH HHS / United States
P01 CA142538 / CA / NCI NIH HHS / United States
R01 AI029168 / AI / NIAID NIH HHS / United States
R01 GM047845 / GM / NIGMS NIH HHS / United States
HHSN268201100005C / HL / NHLBI NIH HHS / United States
HHSN268200900020C / HL / NHLBI NIH HHS / United States