Title | Joint Models of Longitudinal Data and Recurrent Events with Informative Terminal Event. |
Publication Type | Journal Article |
Year of Publication | 2012 |
Authors | Kim, Sehee, Donglin Zeng, Lloyd Chambless, and Yi Li |
Journal | Stat Biosci |
Volume | 4 |
Issue | 2 |
Pagination | 262-281 |
Date Published | 2012 Nov 01 |
ISSN | 1867-1764 |
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. |
DOI | 10.1007/s12561-012-9061-x |
Alternate Journal | Stat Biosci |
Original Publication | Joint models of longitudinal data and recurrent events with informative terminal event. |
PubMed ID | 23227131 |
PubMed Central ID | PMC3516390 |
Grant List | R01 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 |
Joint Models of Longitudinal Data and Recurrent Events with Informative Terminal Event.
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