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Please use this identifier to cite or link to this item: http://hdl.handle.net/1942/8541

Title: A high-dimensional joint model for longitudinal outcomes of different nature
Authors: Faes, Christel
Aerts, Marc
Molenberghs, Geert
Geys, Helena
Teuns, Greet
Bijnens, Luc
Issue Date: 2008
Publisher: JOHN WILEY & SONS LTD
Citation: STATISTICS IN MEDICINE, 27(22). p. 4408-4427
Abstract: In repeated dose-toxicity studies, many outcomes are repeatedly measured on the same animal to study the toxicity of a compound of interest. This is only one example in which one is confronted with the analysis of many outcomes, possibly of a different type. Probably the most common situation is that of an amalgamation of continuous and categorical outcomes. A possible approach towards the joint analysis of two longitudinal outcomes of a different nature is the use of random-effects models (Models for Discrete Longitudinal Data. Springer Series in Statistics. Springer: New York, 2005). Although a random-effects model can easily be extended to jointly model many outcomes of a different nature, computational problems arise as the number of outcomes increases. To avoid maximization of the full likelihood expression, Fieuws and Verbeke (Biometrics 2006; 62:424-431) proposed a pairwise modeling strategy in which all possible pairs are modeled separately, using a mixed model, yielding several different estimates for the same parameters. These latter estimates are then combined into a single set of estimates. Also inference, based on pseudo-likelihood principles, is indirectly derived from the separate analyses. In this paper, we extend the approach of Fieuws and Verbeke (Biometrics 2006; 62:424-431) in two ways: the method is applied to different types of outcomes and the full pseudo-likelihood expression is maximized at once, leading directly to unique estimates as well as direct application of pseudo-likelihood inference. This is very appealing when interested in hypothesis testing. The method is applied to data from a repeated dose-toxicity study designed for the evaluation of the neurofunctional effects of a psychotrophic drug. The relative merits of both methods are discussed. Copyright (c) 2008 John Wiley & Sons, Ltd.
Notes: [Faes, Christel; Aerts, Marc; Molenberghs, Geert] Hasselt Univ, Ctr Stat, Diepenbeek, Belgium. [Geys, Helena; Teuns, Greet; Bijnens, Luc] Johnson & Johnson, PRD Biometr & Clin Informat, Beerse, Belgium.
URI: http://hdl.handle.net/1942/8541
DOI: 10.1002/sim.3314
ISI #: 000259550200003
ISSN: 0277-6715
Category: A1
Type: Journal Contribution
Validation: ecoom, 2009
Appears in Collections: Research publications

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