A Bayesian Joint Model of Longitudinal Kidney Disease Progression, Recurrent Cardiovascular Events, and Terminal Event in Patients with Chronic Kidney Disease

Abstract

AbstractNearly 15% (37 million) of adults in the United States (US) have chronic kidney disease (CKD). The longitudinal decline of kidney function is intricately related to the development of cardiovascular disease (CVD) and eventual “terminal” event (kidney failure and mortality) in patients with CKD. Understanding the mechanism and risk factors underlying the three key outcome processes, (1) CKD progression, (2) CVD, and (3) subsequent terminal event in the CKD patient population remains incomplete. Thus, in this work, we develop a novel trivariate joint model 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 the terminal event. Efficient estimation and inference is proposed within a Bayesian framework using Markov Chain Monte Carlo and Bayesian P-splines for hazard functions. The proposed Bayesian framework is directly generalizable beyond trivariate outcome processes to accommodate other potential modeling of complex multi-disease processes. The method is applied to study the aforementioned trivariate processes using data from the Chronic Renal Insufficiency Cohort Study, an ongoing prospective cohort study, established by the National Institute of Diabetes and Digestive and Kidney Diseases to address the rising epidemic of CKD in the US.