Bayesian hierarchical joint modeling for longitudinal and survival data.

摘要

In studying the evolution of a disease and effects of treatment on it, investigators often collect repeated measures of disease severity (longitudinal data) and measure time to occurrence of a clinical event (survival data). The development of joint models for such longitudinal and survival data often uses individual-specific latent processes that evolve over time and contribute to both the longitudinal and survival outcomes. Such models allow substantial flexibility to incorporate association across repeated measurements, among multiple longitudinal outcomes, and between longitudinal and survival outcomes.;The joint modeling framework has been extended to handle many complexities of real data, but less attention has been paid to the properties of such models. We are interested in the "payoff" of joint modeling, that is, whether using two sources of data simultaneously offers better inference on individual- and population-level characteristics, as compared to using them separately. We consider the problem of attributing informational content to the data inputs of joint models by developing analytical and numerical approaches and demonstrating their use.;As a motivating application, we consider a clinical trial for treatment of mesothelioma, a rapidly fatal form of lung cancer. The trial protocol included patient-reported outcome (PRO) collection throughout the treatment phase and followed patients until progression or death to determine progression-free survival times. We develop models that extend the joint modeling framework to accommodate several features of the longitudinal data, including bounded support, excessive zeros, and multiple PROs measured simultaneously. Our approaches produce clinically relevant treatment effect estimates on several aspects of disease simultaneously and yield insights on individual-level variation in disease processes.