Multiple imputation in health‐are databases: An overview and some applications
Donald B. Rubina b, Nathaniel Schenker Ac
- aDepartment of Statistics, Harvard University, Cambridge, MA 02138
- bDepartment of Statistics, Harvard University, Cambridge, MA 02138, U.S.A
- cDepartment of Biostatistics, School of Public Health, University of California, Los Angeles, CA 90024, U.S.A
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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.
TL;DRThis 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. (Semantic Scholar)