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To analyze the impacts of certain types of public health interventions weneed to estimate the treatment effects and outcomes as these apply toheterogeneous open populations. Dynamically modifying populations containingrisk groups that can react very differently to changes in covariates areinferentially challenging. Here we propose a novel Bayesian graphical modelcalled the Reduced Dynamic Chain Event Graph (RDCEG) customized to suchpopulations. These models generalize the tree-based Chain Event Graphs to aparticular class of graphically supported semi-Markov processes. They providean interface between natural language explanations about what might behappening to individuals and a formal statistical analysis. Here we show howthe RDCEG is able to express the different possible progressions of eachvulnerable individual as well as hypotheses about probabilistic symmetrieswithin these progressions across different individuals within that population.We demonstrate how well-developed Bayesian Network technologies can betransferred almost seamlessly to this class. Our work is motivated by thechallenge of modeling non-pharmacological interventions for recurrent eventprocesses. We illustrate our methodology in two settings: an intervention toreduce falls among the elderly and a trial to examine effects of deferredtreatment among individuals presenting with early epilepsy.

A Bayesian Dynamic Graphical Model for Recurrent Events in Public Health