64th ISI World Statistics Congress - Ottawa, Canada

64th ISI World Statistics Congress - Ottawa, Canada

A Bayesian multilevel time-varying framework for joint modeling of longitudinal and survival outcomes

Conference

64th ISI World Statistics Congress - Ottawa, Canada

Format: IPS Abstract

Keywords: "bayesian, joint models, longitudinal, nonparametric, survival analysis

Session: IPS 161 - Modeling complex correlated data: new directions and innovations

Thursday 20 July 10 a.m. - noon (Canada/Eastern)

Abstract

Over 782 000 individuals in the United States have end-stage kidney disease, with about 72% of patients on dialysis, a life-sustaining treatment. Dialysis patients experience high mortality and frequent hospitalizations, at about twice per year. These poor outcomes are exacerbated at key time periods, such as the fragile period after transition to dialysis. In order to study the time-varying effects of modifiable patient and dialysis facility risk factors on hospitalization and mortality, we propose a novel Bayesian multilevel time-varying joint model. Efficient estimation and inference are achieved within the Bayesian framework using Markov chain Monte Carlo, where multilevel (patient- and dialysis facility-level) varying coefficient functions are targeted via Bayesian P-splines. Applications to the United States Renal Data System, a national database which contains data on nearly all patients on dialysis in the United States, highlight significant time-varying effects of patient- and facility-level risk factors on hospitalization risk and mortality. The finite sample performance of the proposed methodology is studied through simulations.