Advanced Statistics: Missing Data in Clinical Research-Part 2: Multiple Imputation

Craig D. Newgard, Jason S. Haukoos

Research output: Contribution to journalArticlepeer-review

205 Scopus citations


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.

Original languageEnglish (US)
Pages (from-to)669-678
Number of pages10
JournalAcademic Emergency Medicine
Issue number7
StatePublished - Jul 2007


  • bias
  • clinical research
  • imputation
  • missing data
  • multiple imputation
  • statistical analysis

ASJC Scopus subject areas

  • Emergency Medicine


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