Clinically relevant graphical predictions from Bayesian joint longitudinal-survival models

Abstract

Recent interest in understanding the effect of interventions on patient-reported outcomes as well as traditional clinical endpoints has led to an expansion of methods for simultaneous modeling of longitudinal and survival data in clinical trials. Such joint models link the multiple outcome measures using an underlying latent structure, typically a collection of individual-level random effects. They can estimate treatment effects separately on different aspects of a disease process, as well as illuminate associations among outcomes and individual variability. In communicating model output to clinicians and patients, it is challenging to convey a meaningful interpretation of multiple treatment effects, complex outcome associations, and important underlying assumptions. This paper presents graphical displays designed to make the output of Bayesian joint models accessible to non-technical audiences, while preserving important methodological features. We emphasize individual-level posterior predictions of longitudinal and survival outcomes, illustrating our methods using patient-reported symptom severity and survival in a clinical trial example.

Statistical methods for building better biomarkers of chronic kidney disease

“…Data quality is essential to use such complex models with both time‐to‐event and longitudinal parameters in practice. Meaningful clinical interpretation and real applications showing the added value of the more advanced methodologies, such as discovering subgroups exhibiting distinct disease courses, must be demonstrated …”