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|Title: ||High dimensional multivariate mixed models for binary questionnaire data|
|Authors: ||Fieuws, S|
|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.|
|ISI #: ||000239895400002|
|Type: ||Journal Contribution|
|Appears in Collections: ||Research publications|
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