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Resurrecting complete-case analysis: a defense.

Maya B Mathur1,2,3, Ilya Shpitser1,2,3, Tyler J VanderWeele1,2,3

  • 1Quantitative Sciences Unit and Department of Pediatrics, School of Medicine, Stanford University, Palo Alto, CA, United States.

American Journal of Epidemiology
|January 6, 2026
PubMed
Summary
This summary is machine-generated.

Complete-case analysis (CCA) can be a valid method for handling missing data, even with missing not at random (MNAR) data, when appropriate covariates are used. This principled covariate adjustment approach offers a valuable complement to multiple imputation and other MAR-based methods.

Keywords:
causal inferenceimputationmissing dataselection bias

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Area of Science:

  • Statistics
  • Biostatistics
  • Epidemiology

Background:

  • Complete-case analysis (CCA) is often deemed invalid under missing completely at random (MCAR) assumptions.
  • Existing literature frequently recommends abandoning CCA for methods assuming missing at random (MAR).

Purpose of the Study:

  • To advocate for the utility of CCA with principled covariate adjustment.
  • To demonstrate that CCA can complement MAR-based methods like multiple imputation.
  • To highlight CCA's potential validity even with missing not at random (MNAR) data.

Main Methods:

  • Investigated the role of covariate adjustment in Complete-case analysis (CCA).
  • Analyzed causal structures determining bias elimination in CCA.
  • Compared bias reduction in adjusted CCA versus multiple imputation under MAR.

Main Results:

  • Principled covariate adjustment can eliminate bias in CCA, even with MNAR data.
  • Adjusted CCA can be less biased than multiple imputation in certain scenarios.
  • Covariate adjustment often reduces bias in CCA, even when it doesn't eliminate it entirely.

Conclusions:

  • Adjusted CCA is a valuable sensitivity analysis alongside multiple imputation.
  • CCA, with careful covariate control, should be considered a principled method for missing data.
  • There is justification for reinstating CCA as a viable statistical approach.