JSM 2005 - Toronto

Abstract #303593

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Activity Number: 437
Type: Topic Contributed
Date/Time: Wednesday, August 10, 2005 : 2:00 PM to 3:50 PM
Sponsor: Biometrics Section
Abstract - #303593
Title: Random-effects Models for Multivariate Repeated Responses
Author(s): Steffen Fieuws*+ and Geert Verbeke
Companies: Katholieke Universiteit Leuven and Katholieke Universiteit Leuven
Address: Kapucijnenvoer 35, Leuven, 3000, Belgium
Keywords: Joint Modelling ; Mixed Models ; Multivariate Repeated Data ; Pseudolikelihood ; High-Dimensional ; Pairwise Modelling
Abstract:

Multivariate repeated data arise when a set of different outcomes on the same unit is measured repeatedly. One typical example is a longitudinal study where many indices are measured over time. Other examples can be found in clustered settings (e.g., a questionnaire measuring different concepts, each by a set of items). There are situations where a joint modeling approach is more appropriate than modeling each of the outcomes separately. We base a possible joint modeling strategy on the use of mixed models. However, computational problems due to the dimension of the joint covariance matrix of the random effects arise as soon as the number of outcomes increases. We present a pairwise modeling approach, applicable for linear, generalized linear, and nonlinear mixed models, to circumvent this problem. In this approach, we maximize the likelihoods of all pairwise models (each model involves two outcomes) separately instead of the likelihood of the full multivariate model. We obtain estimates for all parameters by averaging over all pairs. Borrowing ideas from the pseudolikelihood framework, standard errors can be calculated.

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Revised March 2005