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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 | Source: | 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. | Keywords: | mixed outcomes; high-dimensional joint model; pseudo-likelihood; longitudinal data;mixed outcomes; high-dimensional joint model; pseudo-likelihood; longitudinal data | Document URI: | http://hdl.handle.net/1942/8541 | ISSN: | 0277-6715 | e-ISSN: | 1097-0258 | DOI: | 10.1002/sim.3314 | ISI #: | 000259550200003 | Rights: | Copyright q 2008 John Wiley & Sons, Ltd. | Category: | A1 | Type: | Journal Contribution | Validations: | ecoom 2009 |
Appears in Collections: | Research publications |
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File | Description | Size | Format | |
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A High-Dimensional Joint Model.pdf | Peer-reviewed author version | 476.32 kB | Adobe PDF | View/Open |
Faes_et_al-2008-Statistics_in_Medicine.pdf Restricted Access | Published version | 194.89 kB | Adobe PDF | View/Open Request a copy |
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