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High Dimensional Multivariate Mixed Models for Binary Questionnaire Data
Steffen Fieuws, Geert Verbeke, Filip Boen and Christophe Delecluse
Journal of the Royal Statistical Society. Series C (Applied Statistics)
Vol. 55, No. 4 (2006), pp. 449-460
Stable URL: http://www.jstor.org/stable/3879102
Page Count: 12
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Questionnaires that are used to measure the effect of an intervention often consist of different sets of items, each set possibly measuring another concept. Mixed models with set-specific random effects are a flexible tool to model the different sets of items jointly. However, computational problems typically arise as the number of sets increases. This is especially true when the random-effects distribution cannot be integrated out analytically, as with mixed models for binary data. A pairwise modelling strategy, in which all possible bivariate mixed models are fitted and where inference follows from pseudolikelihood theory, has been proposed as a solution. This approach has been applied to assess the effect of physical activity on psychocognitive functioning, the latter measured by a battery of questionnaires.
Journal of the Royal Statistical Society. Series C (Applied Statistics) © 2006 Royal Statistical Society