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Please use this identifier to cite or link to this item: http://hdl.handle.net/1942/1472

Title: High dimensional multivariate mixed models for binary questionnaire data
Authors: Fieuws, S
VERBEKE, Geert
Boen, F
Delecluse, C
Issue Date: 2006
Citation: JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES C-APPLIED STATISTICS, 55(4). p. 449-460
Abstract: 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.
URI: http://hdl.handle.net/1942/1472
DOI: 10.1111/j.1467-9876.2006.00546.x
ISI #: 000239895400002
ISSN: 0035-9254
Category: A1
Type: Journal Contribution
Appears in Collections: Research publications

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