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Activity Number: 473 - Advances in Bayesian Methods and Mixture Modeling for Health Data
Type: Topic Contributed
Date/Time: Wednesday, August 10, 2022 : 2:00 PM to 3:50 PM
Sponsor: SSC (Statistical Society of Canada)
Abstract #321015
Title: Bayesian Latent Multi-State Modeling for Health Trajectories
Author(s): Yu Luo*
Companies: Imperial College London
Keywords: Bayesian inference; Continuous-time hidden Markov models; Markov chain Monte Carlo; Reversible jump algorithms; Health trajectories; Longitudinal data analysis
Abstract:

Large amounts of longitudinal health records are now collected in private and public healthcare systems. Data from sources such as electronic health records, healthcare databases and mobile health records are available to inform clinical and public health decision-making. In many situations, such data enable the dynamic monitoring of the underlying disease process that governs the observations. However, this process is not observed directly, and so inferential methods are needed to ascertain progression. Multi-state models capture the status of individuals longitudinally as a discrete-time realization of a continuous-time Markov process. We construct a continuous-time hidden Markov model (CTHMM) with inference based on Markov chain Monte Carlo (MCMC), which provides a fully Bayesian analysis that yields much richer inference than pure-likelihood methods. In addition, we relax the assumption that the number of health states is known by implementing trans-dimensional MCMC that can explore a model space where it is allowed to vary under a CTHMM. Finally, we apply the proposed method to a large COPD dataset from a Canadian healthcare system in Quebec.


Authors who are presenting talks have a * after their name.

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