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Assumptions and analysis planning in studies with missing data in multiple variables: moving beyond the MCAR/MAR/MNAR

Katherine J Lee1,2, John B Carlin1,2,3, Julie A Simpson3

  • 1Clinical Epidemiology and Biostatistics Unit, Murdoch Children's Research Institute, Melbourne, Australia.

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Summary

Handling missing data in research requires careful consideration beyond the traditional MCAR, MAR, and MNAR classifications. This study proposes a new approach focusing on data recoverability and causal relationships for better analysis of incomplete datasets.

Keywords:
Missing datacomplete records analysisdirected acyclic graphsmissing at randommissing not at randommultiple imputationrecoverabilitysensitivity analysis

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Area of Science:

  • Statistics
  • Biostatistics
  • Data Science

Background:

  • Traditional missing data classifications (MCAR, MAR, MNAR) present challenges in multivariable analyses.
  • Assessing MAR plausibility is difficult and often more stringent than assumed.
  • MCAR and MAR are sufficient but not necessary for consistent estimation, complicating analysis choices.

Purpose of the Study:

  • To address limitations of existing missing data classifications.
  • To propose a novel framework for handling multivariable missing data.
  • To guide researchers in selecting appropriate methods for incomplete datasets.

Main Methods:

  • Utilizing directed acyclic graphs (DAGs) to represent missingness assumptions.
  • Defining and assessing 'recoverability' of the target estimand.
  • Linking causal relationships between variables and missingness to estimation strategies.

Main Results:

  • The proposed approach offers a more nuanced understanding of missing data handling.
  • DAGs facilitate clear visualization and assessment of missingness mechanisms.
  • Recoverability provides a criterion for determining the feasibility of consistent estimation.

Conclusions:

  • The traditional MCAR/MAR/MNAR framework is insufficient for complex missing data scenarios.
  • Causal inference and data recoverability are key to selecting optimal missing data methods.
  • This framework enhances the rigor and reliability of analyses with incomplete data.