A1507
Title: Bayesian joint models for longitudinal multimorbidity analysis
Authors: Sida Chen - MRC BSU University of Cambridge (United Kingdom) [presenting]
Danilo Alvares - University of Cambridge (United Kingdom)
Chris Jackson - MRC Biostatistics Unit -Cambridge (United Kingdom)
Sylvia Richardson - MRC Biostatistics - Cambridge (United Kingdom)
Jessica Barrett - MRC Biostatistics Unit (United Kingdom)
Abstract: Multistate models provide a useful framework for modelling complex event history data in clinical settings. They have recently been extended to the joint modelling framework to appropriately handle endogenous longitudinal covariates, such as repeatedly measured biomarkers, which are informative about health status and disease progression. However, the practical application of such joint models faces considerable computational challenges. Motivated by a longitudinal multimorbidity analysis of large-scale UK health records, novel Bayesian inference approaches are introduced for these models that are capable of handling complex multistate processes and large datasets with straightforward implementation. Simulation studies confirm the feasibility of the proposed approaches, with notable advantages in computational efficiency compared to the standard Bayesian estimation strategy. The approaches are used to analyze the coevolution of routinely measured systolic blood pressure (SBP) and the progression of three important chronic conditions based on a large dataset from the clinical practice research datalink aurum database. The analysis reveals distinct and previously lesser-known association structures between SBP and different disease transitions.
