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Acad Emerg Med. 2007 Jul;14(7):669-78.

Advanced statistics: missing data in clinical research--part 2: multiple imputation.

Author information

1
Center for Policy and Research in Emergency Medicine, Department of Emergency Medicine, Oregon Health & Science University, Portland, OR, USA. newgardc@ohsu.edu

Abstract

In part 1 of this series, the authors describe the importance of incomplete data in clinical research, and provide a conceptual framework for handling incomplete data by describing typical mechanisms and patterns of censoring, and detailing a variety of relatively simple methods and their limitations. In part 2, the authors will explore multiple imputation (MI), a more sophisticated and valid method for handling incomplete data in clinical research. This article will provide a detailed conceptual framework for MI, comparative examples of MI versus naive methods for handling incomplete data (and how different methods may impact subsequent study results), plus a practical user's guide to implementing MI, including sample statistical software MI code and a deidentified precoded database for use with the sample code.

PMID:
17595237
DOI:
10.1197/j.aem.2006.11.038
[Indexed for MEDLINE]
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