Joint Models of Longitudinal Data and Recurrent Events with Informative Terminal Event.

TitleJoint Models of Longitudinal Data and Recurrent Events with Informative Terminal Event.
Publication TypeJournal Article
Year of Publication2012
AuthorsKim, Sehee, Donglin Zeng, Lloyd Chambless, and Yi Li
JournalStat Biosci
Volume4
Issue2
Pagination262-281
Date Published2012 Nov 01
ISSN1867-1764
Abstract

This article presents semiparametric joint models to analyze longitudinal data with recurrent event (e.g. multiple tumors, repeated hospital admissions) and terminal event such as death. A broad class of transformation models for the cumulative intensity of the recurrent events and the cumulative hazard of the terminal event is considered, which includes the proportional hazards model and the proportional odds model as special cases. We propose to estimate all the parameters using the nonparametric maximum likelihood estimators (NPMLE). We provide the simple and efficient EM algorithms to implement the proposed inference procedure. Asymptotic properties of the estimators are shown to be asymptotically normal and semiparametrically efficient. Finally, we evaluate the performance of the method through extensive simulation studies and a real-data application.

DOI10.1007/s12561-012-9061-x
Alternate JournalStat Biosci
Original PublicationJoint models of longitudinal data and recurrent events with informative terminal event.
PubMed ID23227131
PubMed Central IDPMC3516390
Grant ListR01 CA082659 / CA / NCI NIH HHS / United States
R01 HL127349 / HL / NHLBI NIH HHS / United States
R01 CA095747 / CA / NCI NIH HHS / United States
R37 GM047845 / GM / NIGMS NIH HHS / United States
U01 HL108642 / HL / NHLBI NIH HHS / United States
P01 CA154295 / CA / NCI NIH HHS / United States
P01 CA142538 / CA / NCI NIH HHS / United States
R01 GM059507 / GM / NIGMS NIH HHS / United States