Multiple imputation in health-care databases: an overview and some applications.
Multiple imputation for non-response replaces each missing value by two or more plausible values. The values can be chosen to represent both uncertainty about the reasons for non-response and uncertainty about which values to impute assuming the reasons for non-response are known. This paper provides an overview of methods for creating and analysing multiply-imputed data sets, and illustrates the dramatic improvements possible when using multiple rather than single imputation. A major application of multiple imputation to public-use files from the 1970 census is discussed, and several exploratory studies related to health care that have used multiple imputation are described.
Rubin
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Department of Statistics, Harvard University, Cambridge, MA 02138.
Schenker
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D016208:Databases, Factual / Q000592:standards*; D003710:Demography; D004812:Epidemiologic Methods; D006302:Health Services Research; D007256:Information Systems / Q000592:standards*; D015233:Models, Statistical; D013223:Statistics as Topic