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Title: Modelling longitudinal claims data using Markov-modulated marked Poisson processes Authors:  Sina Mews - Bielefeld University (Germany) [presenting]
Abstract: Markov-modulated marked Poisson processes (MMMPPs) are explored as a natural framework for modelling patients' disease processes over time based on medical claims data. In claims data, patients' interactions with the healthcare system not only occur at random points in time but are also informative, i.e. driven by unobserved disease levels, as poor health conditions usually lead to more frequent interactions. Therefore, we model the observation process as a Markov-modulated Poisson process, where the rate of healthcare interactions is governed by a continuous-time Markov chain, whose states serve as proxies for the patients' latent disease levels. To provide further information on the latent states, we incorporate additional data collected at each observation time (so-called marks), corresponding to the amount of drug usage, for example, into the model. The distribution of these marks - like the event rates - is determined by the underlying (disease) states. Overall, MMMPPs thus jointly model observations and their informative time points by comprising two state-dependent processes: the observation process (corresponding to the event times) and the mark process (corresponding to event-specific information), which are both driven by an underlying continuous-time Markov chain. The approach is illustrated using claims data from patients diagnosed with chronic obstructive pulmonary disease (COPD), revealing inter-individual differences in the state-switching dynamics.