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SUMMARY:Virtual BSU Seminar: &quot\;Using Variational Bayes for fast infer
 ence in large longitudinal datasets” - Dr David Hughes\, Institute of Po
 pulation Health\, University of Liverpool
DTSTART:20220201T140000Z
DTEND:20220201T150000Z
UID:TALK167750@talks.cam.ac.uk
CONTACT:Alison Quenault
DESCRIPTION:Collecting information on multiple longitudinal outcomes is in
 creasingly 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 variat
 ional Bayes algorithm\, to jointly model multiple Gaussian\, Poisson or bi
 nary longitudinal markers within a multivariate generalised linear mixed m
 odel. Through simulation studies and clinical applications (in the fields 
 of sight threatening diabetic retinopathy and primary biliary cirrhosis) w
 e demonstrate substantial computational savings of our approximate approac
 h when compared to a standard Markov Chain Monte Carlo\, while maintaining
  good levels of accuracy of model parameters.
LOCATION:Virtual Seminar 
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