Joint Modeling of Longitudinal and Cure-survival Data.

TitleJoint Modeling of Longitudinal and Cure-survival Data.
Publication TypeJournal Article
Year of Publication2013
AuthorsKim, Sehee, Donglin Zeng, Yi Li, and Donna Spiegelman
JournalJ Stat Theory Pract
Volume7
Issue2
Pagination324-344
Date Published2013 Apr 01
ISSN1559-8608
Abstract

This article presents semiparametric joint models to analyze longitudinal measurements and survival data with a cure fraction. We consider a broad class of transformations for the cure-survival model, which includes the popular proportional hazards structure and the proportional odds structure 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 demonstrate the good performance of the method through extensive simulation studies and a real-data application.

DOI10.1080/15598608.2013.772036
Alternate JournalJ Stat Theory Pract
Original PublicationJoint modeling of longitudinal and cure-survival data.
PubMed ID23926445
PubMed Central IDPMC3733282
Grant ListR01 CA082659 / CA / NCI NIH HHS / United States
R01 CA095747 / CA / NCI NIH HHS / United States
P01 CA142538 / CA / NCI NIH HHS / United States
P01 CA055075 / CA / NCI NIH HHS / United States
R37 GM047845 / GM / NIGMS NIH HHS / United States
Project: