Statistical Modeling Challenges in Large-scale Population Database: United States Renal Data System

Music City Center 

The United States Renal Data System (USRDS), funded by the National Institute of Diabetes and Digestive and Kidney Diseases, is national data system that collects, analyzes, and disseminate information on chronic kidney disease (CKD) and end-stage kidney disease (ESKD) in the United States (usrds.org). It includes data on nearly all patients on dialysis in the US. In this talk we will discuss several challenges in modeling CKD and ESKD patient outcomes: 1) profiling health-care providers; 2) joint model including multivariate joint modeling of longitudinal, recurrent, and terminal outcomes and spatiotemporal modeling of patient outcomes, including longitudinal hospitalization and mortality. We will present several frequentist and Bayesian approaches to addressing large data size and high-dimensional parameters associated with modeling spatial effects and/or parametrization of time-varying/dynamic effects of risk factors on patient outcomes. The discussion will highlight opportunities and open challenges in modeling patient outcomes using the USRDS database.

Joint modeling

High-dimensional parameters

Time-varying coefficients

Large population database

End-stage kidney disease

Chronic kidney disease 

Biometrics Section