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Please use this identifier to cite or link to this item: http://hdl.handle.net/1942/11149

Title: Testing for misspecification in generalized linear mixed models
Authors: Alonso Abad, Ariel
Litière, Saskia
Molenberghs, Geert
Issue Date: 2010
Citation: BIOSTATISTICS, 11(4). p. 771-786
Abstract: Generalized linear mixed models have become a frequently used tool for the analysis of non-Gaussian longitudinal data. Estimation is often based on maximum likelihood theory, which assumes that the underlying probability model is correctly specified. Recent research shows that the results obtained from these models are not always robust against departures from the assumptions on which they are based. Therefore, diagnostic tools for the detection of model misspecifications are of the utmost importance. In this paper, we propose 2 diagnostic tests that are based on 2 equivalent representations of the model information matrix. We evaluate the power of both tests using theoretical considerations as well as via simulation. In the simulations, the performance of the new tools is evaluated in many settings of practical relevance, focusing on misspecification of the random-effects structure. In all the scenarios, the results were encouraging, however, the tests also exhibited inflated Type I error rates when the sample size was small or moderate. Importantly, a parametric bootstrap version of the tests seems to overcome this problem, although more research in this direction may be needed. Finally, both tests were also applied to analyze a real case study in psychiatry.
Notes: [Abad, Ariel Alonso; Litiere, Saskia; Molenberghs, Geert] Hasselt Univ, Interuniv Inst Biostat & Stat Bioinformat, B-3590 Diepenbeek, Belgium. [Molenberghs, Geert] Katholieke Univ Leuven, B-3000 Louvain, Belgium. ariel.alonso@uhasselt.be
URI: http://hdl.handle.net/1942/11149
DOI: 10.1093/biostatistics/kxq019
ISI #: 000281342400014
ISSN: 1465-4644
Category: A1
Type: Journal Contribution
Validation: ecoom, 2011
Appears in Collections: Research publications

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