Using empirical Bayes predictors from generalized linear mixed models to test and visualize associations among longitudinal outcomes

Statistical Methods in Medical Research
Susan K Mikulich-GilbertsonGary O Zerbe

Abstract

Medical research is often designed to investigate changes in a collection of response variables that are measured repeatedly on the same subjects. The multivariate generalized linear mixed model (MGLMM) can be used to evaluate random coefficient associations (e.g. simple correlations, partial regression coefficients) among outcomes that may be non-normal and differently distributed by specifying a multivariate normal distribution for their random effects and then evaluating the latent relationship between them. Empirical Bayes predictors are readily available for each subject from any mixed model and are observable and hence, plotable. Here, we evaluate whether second-stage association analyses of empirical Bayes predictors from a MGLMM, provide a good approximation and visual representation of these latent association analyses using medical examples and simulations. Additionally, we compare these results with association analyses of empirical Bayes predictors generated from separate mixed models for each outcome, a procedure that could circumvent computational problems that arise when the dimension of the joint covariance matrix of random effects is large and prohibits estimation of latent associations. As has been shown in ot...Continue Reading

References

Sep 28, 2004·Statistics in Medicine·Steffen Fieuws, Geert Verbeke
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Jul 28, 2007·Statistical Methods in Medical Research·S FieuwsG Molenberghs
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Nov 5, 2016·Statistics in Medicine·Matthew Bryan, Patrick J Heagerty

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Citations

Sep 30, 2021·Health Services Research·Alistair James O'MalleyJohn Michael McWilliams
Nov 11, 2021·Clinical Transplantation·Chiara BecchettiGiacomo Germani

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Methods Mentioned

BETA
pharmacotherapy

Software Mentioned

SAS
NLMIXED
SAS IML

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