A0238
Title: A Bayesian trivariate joint model of kidney disease progression, recurrent events, and terminal event in CKD
Authors: Danh Nguyen - University of California, Irvine (United States) [presenting]
Abstract: Nearly 37 million adults in the U.S. have chronic kidney disease (CKD). The longitudinal trajectory of kidney function decline in patients with CKD is intricately related to the development of cardiovascular disease (CVD) and eventual "terminal" events (kidney failure and mortality). Understanding of the mechanism and risk factors underlying the three key outcome processes, (1) CKD progression, (2) CVD, and (3) subsequent terminal events in the CKD patient population, remains incomplete. Thus, a novel trivariate joint model is developed to study the risk factors associated with the interdependent outcomes of kidney function (as measured by longitudinal estimated glomerular filtration rate), recurrent cardiovascular events, and terminal events. Efficient estimation and inference are proposed within a Bayesian framework using Markov Chain Monte Carlo and Bayesian P-splines for hazard functions. The method is applied to study the aforementioned trivariate processes using data from the Chronic Renal Insufficiency Cohort Study, an ongoing prospective cohort study.
