Survival analysis with electronic health record data: Experiments with chronic kidney disease. (19th August 2014)

• Record Type: Journal Article
• Authors: Hagar, Yolanda; Albers, David; Pivovarov, Rimma; Chase, Herbert; Dukic, Vanja; Elhadad, Noémie
• Is Part Of: Statistical analysis and data mining. Volume 7:Number 5(2014)
• Page Start: 385
• Page End: 403
• Publication Date: 2014-08-19
• Subjects: Data mining -- Statistical methods -- Periodicals; 006.312
• DOI: 10.1002/sam.11236
• Languages: English
• ISSNs: 1932-1864

Abstract

<abstract abstract-type="main" xml:lang="en"> <title>Abstract</title> <p>This article presents a detailed survival analysis for chronic kidney disease (CKD). The analysis is based on the electronic health record (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.</p> </abstract>