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Title: | High dimensional multivariate mixed models for binary questionnaire data | Authors: | FIEUWS, Steffen VERBEKE, Geert Boen, F Delecluse, C |
Issue Date: | 2006 | Source: | 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. | Keywords: | generalized linear mixed model; high dimensional mixed model; joint model; multidimensional item response theory model; multivariate mixed model; pseudolikelihood; LIKELIHOOD | Document URI: | http://hdl.handle.net/1942/1472 | ISSN: | 0035-9254 | e-ISSN: | 1467-9876 | DOI: | 10.1111/j.1467-9876.2006.00546.x | ISI #: | 000239895400002 | Category: | A1 | Type: | Journal Contribution |
Appears in Collections: | Research publications |
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