title: Multiple imputation in health-care databases: an overview and some applications.
abstract: 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.
authors.affiliation: Department of Statistics, Harvard University, Cambridge, MA 02138.
authors.forename: D B
authors.initials: DB
authors.lastname: Rubin
authors.forename: N
authors.initials: N
authors.lastname: Schenker
mesh_terms: 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