Multiple imputation versus data enhancement for dealing with missing data in observational health care outcome analyses
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...
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| Veröffentlicht in: | Journal of clinical epidemiology 2002-02, Vol.55 (2), p.184-191 |
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| container_title | Journal of clinical epidemiology |
| container_volume | 55 |
| creator | Faris, Peter D Ghali, William A Brant, Rollin Norris, Colleen M Galbraith, P.Diane Knudtson, Merril L |
| description | 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. |
| doi_str_mv | 10.1016/S0895-4356(01)00433-4 |
| format | Article |
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Alberta Provincial Program for Outcome Assessment in Coronary Heart Disease</creatorcontrib><title>Multiple imputation versus data enhancement for dealing with missing data in observational health care outcome analyses</title><title>Journal of clinical epidemiology</title><addtitle>J Clin Epidemiol</addtitle><description>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.</description><subject>Administrative data</subject><subject>Alberta - epidemiology</subject><subject>Angiography</subject><subject>Biological and medical sciences</subject><subject>Cardiac Catheterization - statistics & numerical data</subject><subject>Cardiovascular</subject><subject>Cardiovascular Diseases - epidemiology</subject><subject>Clinical data</subject><subject>Databases, Factual</subject><subject>Epidemiologic Methods</subject><subject>Epidemiology</subject><subject>General aspects</subject><subject>Humans</subject><subject>Logistic Models</subject><subject>Medical sciences</subject><subject>Methodology</subject><subject>Middle Aged</subject><subject>Multiple imputation</subject><subject>Predictive Value of Tests</subject><subject>Public health. Hygiene</subject><subject>Public health. 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| source | MEDLINE; Elsevier ScienceDirect Journals Complete |
| subjects | Administrative data Alberta - epidemiology Angiography Biological and medical sciences Cardiac Catheterization - statistics & numerical data Cardiovascular Cardiovascular Diseases - epidemiology Clinical data Databases, Factual Epidemiologic Methods Epidemiology General aspects Humans Logistic Models Medical sciences Methodology Middle Aged Multiple imputation Predictive Value of Tests Public health. Hygiene Public health. Hygiene-occupational medicine Research Design - statistics & numerical data |
| title | Multiple imputation versus data enhancement for dealing with missing data in observational health care outcome analyses |
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