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What is the difference between missing completely at random and missing at random?

Krishnan Bhaskaran1, Liam Smeeth2

  • 1Department of Non-communicable Diseases Epidemiology, London School of Hygiene and Tropical Medicine, London, UK krishnan.bhaskaran@lshtm.ac.uk.

International Journal of Epidemiology
|April 8, 2014
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Summary
This summary is machine-generated.

Understanding missing data is crucial for researchers. This article clarifies the difference between missing at random and missing completely at random to guide appropriate statistical methods like multiple imputation.

Keywords:
missing at randommissing datamultiple imputation

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Area of Science:

  • Statistics
  • Biostatistics
  • Data Science

Background:

  • Confusing terminology surrounding missing data mechanisms hinders accurate analysis.
  • Misinterpretation of 'missing at random' can lead researchers to avoid beneficial statistical techniques.

Purpose of the Study:

  • To clarify the distinction between 'missing at random' and 'missing completely at random'.
  • To guide researchers in selecting appropriate methods for handling missing data, such as multiple imputation.

Main Methods:

  • An imagined dialogue between a clinical researcher and a statistician is used to illustrate the concepts.
  • Conceptual explanation of missing data mechanisms.

Main Results:

  • The dialogue effectively differentiates 'missing at random' from 'missing completely at random'.
  • Highlights the practical implications of understanding these distinctions for data analysis.

Conclusions:

  • Clearer understanding of missingness mechanisms improves statistical analysis.
  • Promotes the appropriate application of advanced methods like multiple imputation for missing data problems.