Title | Joint Modeling of Longitudinal and Cure-survival Data. |
Publication Type | Journal Article |
Year of Publication | 2013 |
Authors | Kim, Sehee, Donglin Zeng, Yi Li, and Donna Spiegelman |
Journal | J Stat Theory Pract |
Volume | 7 |
Issue | 2 |
Pagination | 324-344 |
Date Published | 2013 Apr 01 |
ISSN | 1559-8608 |
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. |
DOI | 10.1080/15598608.2013.772036 |
Alternate Journal | J Stat Theory Pract |
Original Publication | Joint modeling of longitudinal and cure-survival data. |
PubMed ID | 23926445 |
PubMed Central ID | PMC3733282 |
Grant List | R01 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 |
Joint Modeling of Longitudinal and Cure-survival Data.
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