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Joint models (JM) for longitudinal and survival data have gained increasinginterest and found applications in a wide range of clinical and biomedicalsettings. These models facilitate the understanding of the relationship betweenoutcomes and enable individualized predictions. In many applications, morecomplex event processes arise, necessitating joint longitudinal and multistatemodels. However, their practical application can be hindered by computationalchallenges due to increased model complexity and large sample sizes. Motivatedby a longitudinal multimorbidity analysis of large UK health records, we havedeveloped a scalable Bayesian methodology for such joint multistate models thatis capable of handling complex event processes and large datasets, withstraightforward implementation. We propose two blockwise inference approachesfor different inferential purposes based on different levels of decompositionof the multistate processes. These approaches leverage parallel computing, easethe specification of different models for different transitions, andmodel/variable selection can be performed within a Bayesian framework usingBayesian leave-one-out cross-validation. Using a simulation study, we show thatthe proposed approaches achieve satisfactory performance regarding posteriorpoint and interval estimation, with notable gains in sampling efficiencycompared to the standard estimation strategy. We illustrate our approachesusing a large UK electronic health record dataset where we analysed thecoevolution of routinely measured systolic blood pressure (SBP) and theprogression of multimorbidity, defined as the combinations of three chronicconditions. Our analysis identified distinct association structures between SBPand different disease transitions.

Bayesian blockwise inference for joint models of longitudinal and multistate processes