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Accounting for missing data in statistical analyses: multiple imputation is not always the answer.

Rachael A Hughes1,2, Jon Heron1,2,3, Jonathan A C Sterne1,3

  • 1Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK.

International Journal of Epidemiology
|March 18, 2019
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Summary
This summary is machine-generated.

Complete case analysis (CCA) can be better than multiple imputation (MI) for missing data in epidemiology. Careful consideration of missing data reasons, patterns, and auxiliary information is crucial for choosing the right analysis method.

Keywords:
Complete case analysisinverse probability weightingmissing datamissing data mechanismsmissing data patternsmultiple imputation

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Area of Science:

  • Epidemiological research
  • Statistical analysis

Background:

  • Missing data is common in epidemiology, potentially causing bias and reduced precision.
  • Multiple imputation (MI) is often favored over complete case analysis (CCA) for handling missing data.
  • However, CCA may be preferable to MI in specific scenarios.

Purpose of the Study:

  • To provide guidance on selecting appropriate analysis methods for incomplete epidemiological data.
  • To compare the bias and efficiency of MI and CCA under various missing data mechanisms.
  • To illustrate practical selection of methods for missing data.

Main Methods:

  • Utilized causal diagrams to represent missingness mechanisms.
  • Assessed conditions under which CCA yields unbiased results.
  • Compared MI and CCA in terms of bias and efficiency across different missing data situations.

Main Results:

  • CCA provides unbiased results when complete case status is independent of the outcome, including some missing-not-at-random situations.
  • In such cases, MI assuming missing-at-random (MAR) may be biased, while CCA remains unbiased.
  • MI is valid for all MAR situations and can improve precision and reduce bias by using auxiliary information and incomplete cases.

Conclusions:

  • The choice of method for addressing missing data is critical for the validity of research findings.
  • Selection should be informed by the reasons for missingness, data patterns, and available auxiliary information.