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Survival Analysis with Electronic Health Record Data: Experiments with Chronic Kidney Disease
Yolanda Hagar1, David Albers1, Rimma Pivovarov1
1Yolanda Hagar is a postdoctoral researcher in applied mathematics at the University of Colorado at Boulder. David Albers is an associate research scientist in biomedical informatics at Columbia University. Rimma Pivovarov is a doctoral candidate in biomedical informatics at Columbia University. Herbert Chase is a professor of clinical medicine in biomedical informatics at Columbia University. Vanja Dukic is an associate professor in applied mathematics at the University of Colorado at Boulder. Noémie Elhadad is an assistant professor in biomedical informatics at Columbia University.
Abstract:
This paper presents a detailed survival analysis for chronic kidney disease (CKD). The analysis is based on the EHR data comprising almost two decades of clinical observations collected at New York-Presbyterian, a large hospital in New York City with one of the oldest electronic health records in the United States. Our survival analysis approach centers around Bayesian multiresolution hazard modeling, with an objective to capture the changing hazard of CKD over time, adjusted for patient clinical covariates and kidney-related laboratory tests. Special attention is paid to statistical issues common to all EHR data, such as cohort definition, missing data and censoring, variable selection, and potential for joint survival and longitudinal modeling, all of which are discussed alone and within the EHR CKD context.