High Dimensional Multivariate Mixed Models for Binary Questionnaire Data
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...
| Veröffentlicht in: | Journal of the Royal Statistical Society. Series C (Applied Statistics). - Blackwell Publishers. - 55(2006), 4, Seite 449-460 |
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| Format: | Online-Aufsatz |
| Sprache: | English |
| Veröffentlicht: |
2006
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| Zugriff auf das übergeordnete Werk: | Journal of the Royal Statistical Society. Series C (Applied Statistics) |
| Schlagworte: | Generalized Linear Mixed Model High Dimensional Mixed Model Joint Model Multidimensional Item Response Theory Model Multivariate Mixed Model Pseudolikelihood Applied sciences Mathematics Philosophy Health sciences mehr... |
| Zusammenfassung: | 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. |
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| ISSN: | 14679876 |