BSU Seminar: "Bayesian latent multi-state modelling for longitudinal health trajectories"

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• Yu Luo, Kings College London
• Tuesday 15 October 2024, 14:00-15:00
• MRC Biostatistics Unit, East Forvie Building, Forvie Site Robinson Way Cambridge CB2 0SR..

If you have a question about this talk, please contact Alison Quenault.

This will be a free hybrid seminar. To register to attend virtually, please click here: https://cam-ac-uk.zoom.us/meeting/register/tZIqcuutqTkoG9R7enI9iOHBr61dOjW5BTKl

In medical research, understanding changes in outcome measurements is crucial for inferring shifts in a patient’s underlying condition. While data from clinical and administrative systems hold promise for advancing this understanding, traditional methods for modelling disease progression struggle with analyzing a huge volume of longitudinal data collected irregularly and do not account for the phenomenon where the poorer an individual’s health, the more frequently they interact with the healthcare system. In addition, data from the claim and health care system provide no information for terminating event, such as death. To address these challenges, we develop a Bayesian approach for the continuous-time hidden Markov model (CTHMM) to understand disease progression by modelling the observed data as an outcome whose distribution depends on the state of a latent Markov chain representing the underlying health state. Subsequently, we extend the model and allow the underlying health state to influence the timings of the observations via a point process and the unobserved death as an informative censoring whose rate depends on the latent state of the Markov chain. This extension allows us to model disease severity and death not only based on the types of care received but also on the temporal and frequency aspects of different observed events. In addition, we also consider the death as missing data, and model it as an informative censoring. We present an exact Gibbs sampler procedure that integrates the observation process by modelling observed data as an outcome process and the timing of observations as a point process, jointly with the CTHMM , given the underlying latent health state. We facilitate our inference by recovering the trajectories from the final observed record to the end of the observation period, thereby mitigating the bias caused by the lack of information from early death patients. Finally, we apply our method to health care claim data from a Canadian cohort.

This talk is part of the MRC Biostatistics Unit Seminars series.

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