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Please use this identifier to cite or link to this item: http://hdl.handle.net/1942/8343

Title: The impact of a misspecified random-effects distribution on the estimation and the performance of inferential procedures in generalized linear mixed models
Authors: Litière, Saskia
Alonso Abad, Ariel
Molenberghs, Geert
Issue Date: 2008
Publisher: Wiley
Citation: STATISTICS IN MEDICINE, 27(16). p. 3125-3144
Abstract: Estimation in generalized linear mixed models is often based on maximum likelihood theory, assuming that the underlying probability model is correctly specified. However, the validity of this assumption is sometimes difficult to verify. In this paper we study, through simulations, the impact of misspecifying the random-effects distribution on the estimation and hypothesis testing in generalized linear mixed models. It is shown that the maximum likelihood estimators are inconsistent in the presence of misspecification. The bias induced in the mean structure parameters is generally small, as far as the variability of the underlying random-effects distribution is small as well. However, the estimates of this variability are always severely biased. Given that the variance components are the only tool to study the variability of the true distribution, it is difficult to assess whether problems in the estimation of the mean structure occur. The Type I error rate and the power of the commonly used inferential procedures are also severely affected. The situation is aggravated if more than one random effect is included in the model. Further, we propose to deal with possible misspecification by way of sensitivity analysis, considering several random-effects distributions. All the results are illustrated using data from a clinical trial in schizophrenia.
URI: http://hdl.handle.net/1942/8343
DOI: 10.1002/sim.3157
ISI #: 000257567900009
ISSN: 0277-6715
Category: A1
Type: Journal Contribution
Validation: ecoom, 2009
Appears in Collections: Research publications

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