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.
ISSN 1465-4644, 1468-4357
|
Text
Hughesetal_MFVBmglmm_Biostatistics_Revision1.pdf - Author Accepted Manuscript Download (308kB) | Preview |
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: | 07 Dec 2024 22:59 |
| DOI: | 10.1093/biostatistics/kxab021 |
| Open Access URL: | https://doi.org/10.1093/biostatistics/kxab021 |
| Related URLs: | |
| URI: | https://livrepository.liverpool.ac.uk/id/eprint/3121879 |
Altmetric
Altmetric
