title: Multiple imputation versus data enhancement for dealing with missing data in observational health care outcome analyses.
abstract: The problem of missing data is frequently encountered in observational studies. We compared approaches to dealing with missing data. Three multiple imputation methods were compared with a method of enhancing a clinical database through merging with administrative data. The clinical database used for comparison contained information collected from 6,065 cardiac care patients in 1995 in the province of Alberta, Canada. The effectiveness of the different strategies was evaluated using measures of discrimination and goodness of fit for the 1995 data. The strategies were further evaluated by examining how well the models predicted outcomes in data collected from patients in 1996. In general, the different methods produced similar results, with one of the multiple imputation methods demonstrating a slight advantage. It is concluded that the choice of missing data strategy should be guided by statistical expertise and data resources.
authors.affiliation: Community Health Sciences, University of Calgary, 3330 Hospital Drive NW Calgary, Alberta, T2N 4N1 Canada.
authors.forename: Peter D
authors.initials: PD
authors.lastname: Faris
authors.forename: William A
authors.initials: WA
authors.lastname: Ghali
authors.forename: Rollin
authors.initials: R
authors.lastname: Brant
authors.forename: Colleen M
authors.initials: CM
authors.lastname: Norris
authors.forename: P Diane
authors.initials: PD
authors.lastname: Galbraith
authors.forename: Merril L
authors.initials: ML
authors.lastname: Knudtson
mesh_terms: D000416:Alberta / Q000453:epidemiology; D006328:Cardiac Catheterization / Q000706:statistics & numerical data*; D002318:Cardiovascular Diseases / Q000453:epidemiology; D016208:Databases, Factual; D004812:Epidemiologic Methods; D006801:Humans; D016015:Logistic Models; D008875:Middle Aged; D011237:Predictive Value of Tests; D012107:Research Design / Q000706:statistics & numerical data*