Fast approximate inference for multivariate longitudinal data

Hughes, David M ORCID: 0000-0002-1287-9994, Garcia-Finana, Marta ORCID: 0000-0003-4939-0575 and Wand, Matt P (2022) Fast approximate inference for multivariate longitudinal data. BIOSTATISTICS, 24 (1). pp. 177-192.

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Official URL: http://dx.doi.org/10.1093/biostatistics/kxab021

Abstract

Collecting information on multiple longitudinal outcomes is increasingly common in many clinical settings. In many cases, it is desirable to model these outcomes jointly. However, in large data sets, with many outcomes, computational burden often prevents the simultaneous modeling of multiple outcomes within a single model. We develop a mean field variational Bayes algorithm, to jointly model multiple Gaussian, Poisson, or binary longitudinal markers within a multivariate generalized linear mixed model. Through simulation studies and clinical applications (in the fields of sight threatening diabetic retinopathy and primary biliary cirrhosis), we demonstrate substantial computational savings of our approximate approach when compared to a standard Markov Chain Monte Carlo, while maintaining good levels of accuracy of model parameters.

Item Type:	Article
Uncontrolled Keywords:	Bayesian computing, Generalized linear mixed model, Markov chain Monte Carlo, Mean field variational Bayes, Multivariate mixed models, Repeated measurements
Divisions:	Faculty of Health and Life Sciences Faculty of Health and Life Sciences > Institute of Population Health
Depositing User:	Symplectic Admin
Date Deposited:	07 May 2021 15:01
Last Modified:	18 Jan 2023 22:49
DOI:	10.1093/biostatistics/kxab021
Open Access URL:	https://doi.org/10.1093/biostatistics/kxab021
Related URLs:	Author Publisher
URI:	https://livrepository.liverpool.ac.uk/id/eprint/3121879