Advanced Statistics: Missing Data in Clinical Research-Part 1: An Introduction and Conceptual Framework

Jason S. Haukoos, Craig D. Newgard

Research output: Contribution to journalArticlepeer-review

114 Scopus citations

Abstract

Missing data are commonly encountered in clinical research. Unfortunately, they are often neglected or not properly handled during analytic procedures, and this may substantially bias the results of the study, reduce study power, and lead to invalid conclusions. In this two-part series, the authors will introduce key concepts regarding missing data in clinical research, provide a conceptual framework for how to approach missing data in this setting, describe typical mechanisms and patterns of censoring of data and their relationships to specific methods of handling incomplete data, and describe in detail several simple and more complex methods of handling such data. In part 1, the authors will describe relatively simple approaches to handling missing data, including complete-case analysis, available-case analysis, and several forms of single imputation, including mean imputation, regression imputation, hot and cold deck imputation, last observation carried forward, and worst case analysis. In part 2, the authors will describe in detail multiple imputation, a more sophisticated and valid method for handling missing data.

Original languageEnglish (US)
Pages (from-to)662-668
Number of pages7
JournalAcademic Emergency Medicine
Volume14
Issue number7
DOIs
StatePublished - Jul 1 2007
Externally publishedYes

Keywords

  • bias
  • clinical research
  • complete-case analysis
  • hot deck imputation
  • imputation
  • last observation carried forward
  • mean imputation
  • missing data
  • regression imputation
  • single imputation
  • statistical analysis
  • worst case analysis

ASJC Scopus subject areas

  • Emergency Medicine

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