Title: “Using Variational Bayes for fast inference in large longitudinal datasets”
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 datasets, with many outcomes, computational burden often prevents the simultaneous modelling 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 generalised 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.
This will be a free virtual seminar. If you would like to join, please register here: BSU Virtual Seminar – Dr David Hughes Tickets, Tue 1 Feb 2022 at 14:00 | Eventbrite.
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