title: Building predictive medical models on incomplete data.
abstract: While working on the National Cardiovascular Network (NCN) Outcomes Management Report our group was confronted with a high percentage of missing data, despite the large size of our registry. One of our goals was to find a way to compare the results achieved at different sites. Excluding cases with missing data significantly decreased the number of cases and, in some instances, all the data from a particular center was eliminated, thereby removing them from comparison. To avoid such a scenario, we utilized multiple imputation. The obtained results and methods used are subject of this article.
authors.affiliation: Emory University, School of Medicine, Atlanta, USA.
authors.forename: Emir
authors.initials: E
authors.lastname: Veledar
authors.forename: Trevor
authors.initials: T
authors.lastname: Thompson
authors.forename: Chu
authors.initials: C
authors.lastname: Haitao
mesh_terms: D015906:Angioplasty, Balloon, Coronary / Q000401:mortality; D002318:Cardiovascular Diseases; D001026:Coronary Artery Bypass / Q000401:mortality; D003625:Data Collection; D016208:Databases, Factual; D017052:Hospital Mortality; D006801:Humans; D020379:Risk Adjustment