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Please use this identifier to cite or link to this item: http://hdl.handle.net/1942/1956

Title: Type I and type II error under random-effects misspecification in generalized linear mixed models
Authors: Litière, Saskia
Alonso Abad, Ariel
Molenberghs, Geert
Issue Date: 2007
Citation: BIOMETRICS, 63(4). p. 1038-1044
Abstract: Generalized linear mixed models (GLMMs) have become a frequently used tool for the analysis of non-Gaussian longitudinal data. Estimation is based on maximum likelihood theory, which assumes that the underlying probability model is correctly specified. Recent research is showing that the results obtained from these models are not always robust against departures from the assumptions on which these models are based. In the present work we have used simulations with a logistic random-intercept model to study the impact of misspecifying the random-effects distribution on the type I and II errors of the tests for the mean structure in GLMMs. We found that the misspecification can either increase or decrease the power of the tests, depending on the shape of the underlying random-effects distribution, and it can considerably inflate the type I error rate. Additionally, we have found a theoretical result which states that whenever a subset of fixed-effects parameters, not included in the random-effects structure equals zero, the corresponding maximum likelihood estimator will consistently estimate zero. This implies that under certain conditions a significant effect could be considered as a reliable result, even if the random-effects distribution is misspecified.
URI: http://hdl.handle.net/1942/1956
DOI: 10.1111/j.1541-0420.2007.00782.x
ISI #: 000251508300007
ISSN: 0006-341X
Category: A1
Type: Journal Contribution
Validation: ecoom, 2009
Appears in Collections: Research publications

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