A Bayesian model for joint analysis of multivariate repeated measures and time to event data in crossover trials

Joint modeling of longitudinal and survival data has become a popular technique in analyzing longitudinal clinical trials. In this discussion, the potentials of joint modeling are explored for analyzing time to event and multivariate repeated measures in crossover studies. The work is motivated by a real-life crossover study with three visual analog scale responses and a time to event response. To recover the information lost due to censoring of the time to event variable, we propose a Bayesian joint model to analyze the visual analog scale and time to event responses jointly, leveraging the moderate associations among the responses. The joint model links the time to event variable to the visual analog scale repeated measures via multi-layered subject-specific random effects. We show the Bayesian joint model produces more efficient inferences with satisfactory goodness of fit in general with comparison to modeling of the visual analog scale and time to event responses separately. A simulation study is performed to demonstrate the inferential advantages of Bayesian joint model over separate modeling and maximum likelihood approaches via non-linear mixed modeling in the crossover setting. This work also demonstrates the flexibility and usefulness of zero-one inflated beta regression in modeling non-Gaussian fixed-boundaries-inflated outcomes in general.