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High dimensional multivariate mixed models for binary questionnaire data
Author(s) -
Fieuws Steffen,
Verbeke Geert,
Boen Filip,
Delecluse Christophe
Publication year - 2006
Publication title -
journal of the royal statistical society: series c (applied statistics)
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.205
H-Index - 72
eISSN - 1467-9876
pISSN - 0035-9254
DOI - 10.1111/j.1467-9876.2006.00546.x
Subject(s) - bivariate analysis , pairwise comparison , random effects model , binary number , binary data , multivariate statistics , mixed model , set (abstract data type) , inference , measure (data warehouse) , computer science , data set , statistics , mathematics , data mining , artificial intelligence , medicine , meta analysis , arithmetic , programming language
Summary. 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.