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

Acad Emerg Med. 2007 Jul;14(7):669-78. doi: 10.1197/j.aem.2006.11.038.


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.

MeSH terms

  • Accidents, Traffic / statistics & numerical data
  • Biomedical Research*
  • Data Interpretation, Statistical
  • Humans
  • Logistic Models
  • Research Design
  • Software
  • Statistics as Topic*