Fast approximate inference for multivariate longitudinal data

18^th November 2020, 11:00
David Hughes

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.

Biography

David is currently a UKRI Innovation Fellow funded by the MRC, investigating variational approximations in longitudinal data analysis. My research involves approximate computationally efficient methods of estimating mixed models for multivariate longitudinal data. I'm also working on jointly modelling longitudinal and survival data. Most of this work is focused on clinical risk prediction methods, and covers applications involving diabetes, epilepsy and liver cancer.