20: A Fully Bayesian Joint Modeling Framework for Complex Intercurrent Event Handling

Music City Center 

We propose a fully Bayesian joint modeling framework for analyzing longitudinal outcomes in the presence of one or more intercurrent events. Our innovative approach leverages a pattern-mixture model consisting of a marginal distribution for the intercurrent event(s) and a conditional distribution for the longitudinal outcome given the intercurrent event times. We demonstrate how, with this model, one can represent complex estimands (i.e., marginal treatment effects) intuitively as functions of model parameters from the marginal and conditional models. The framework is applied to a case study from a recent trial involving two intercurrent events - one addressed with a hypothetical strategy and the other with a composite strategy.