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Please use this identifier to cite or link to this item: http://hdl.handle.net/1942/24012

Title: A flexible joint modeling framework for longitudinal and time-to-event data with overdispersion
Authors: Njagi, Edmund N.
Molenberghs, Geert
Rizopoulos, Dimitris
Verbeke, Geert
Kenward, Michael G.
Dendale, Paul
Willekens, Koen
Issue Date: 2016
Abstract: We combine conjugate and normal random effects in a joint model for outcomes, at least one of which is non-Gaussian, with particular emphasis on cases in which one of the outcomes is of survival type. Conjugate random effects are used to relax the often-restrictive mean-variance prescription in the non-Gaussian outcome, while normal random effects account for not only the correlation induced by repeated measurements from the same subject but also the association between the different outcomes. Using a case study in chronic heart failure, we show that model fit can be improved, even resulting in impact on significance tests, by switching to our extended framework. By first taking advantage of the ease of analytical integration over conjugate random effects, we easily estimate our framework, by maximum likelihood, in standard software.
URI: http://hdl.handle.net/1942/24012
DOI: 10.1177/0962280213495994
ISI #: 000382871200035
ISSN: 0962-2802
Category: A1
Type: Journal Contribution
Validation: ecoom, 2017
Appears in Collections: Research publications

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