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Approaches for missing covariate data in logistic regression with MNAR sensitivity analyses.

Ralph C Ward1, Robert Neal Axon2, Mulugeta Gebregziabher2

  • 1Department of Public Health Sciences, Medical University of South Carolina, Charleston, SC, USA.

Biometrical Journal. Biometrische Zeitschrift
|January 21, 2020
PubMed
Summary

Complete case analysis (CCA) and multiple imputation (MI) can both yield unbiased results for missing covariate data. MI-FCS performed comparably to CCA, and was superior when missingness involved categorical covariates.

Keywords:
logistic regressionmissing covariatesmultiple imputationpredictable missingness sensitivity analysis

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Area of Science:

  • Statistics
  • Data Science
  • Biostatistics

Background:

  • Missing covariate data with fully observed binary outcomes present a significant challenge in statistical analysis.
  • Complete case analysis (CCA) and multiple imputation (MI) are common methods, with CCA assuming missing completely at random (MCAR) and MI typically assuming missing at random (MAR).

Purpose of the Study:

  • To compare the performance of various machine learning and parametric multiple imputation methods under a fully conditional specification framework (MI-FCS).
  • To evaluate these methods across different missing data mechanisms, including missing completely at random (MCAR), missing at random (MAR), and missing not at random (MNAR).

Main Methods:

  • A simulation study was conducted with five scenarios encompassing MCAR, MAR, and MNAR (predictable and non-predictable conditions).
  • Performance was assessed by comparing multiple imputation methods (MI-FCS) against complete case analysis (CCA).
  • Missing not at random (MNAR) sensitivity analysis was employed to assess the robustness of results.

Main Results:

  • Both MI and CCA can produce unbiased results under a wider range of conditions than commonly assumed, including certain missing not at random (MNAR) scenarios.
  • When both MI-FCS and CCA were valid, MI-FCS demonstrated comparable or superior performance in terms of bias and coverage, especially when missingness involved categorical covariates.
  • MNAR sensitivity analysis confirmed the validity of results under specific MNAR conditions when both CCA and MI were applicable.

Conclusions:

  • Multiple imputation (MI) and complete case analysis (CCA) are more versatile than often recognized for handling missing covariate data.
  • Investigators should consider comparing results from both MI and CCA when both methods are plausibly valid.
  • Employing MNAR sensitivity analysis is recommended to build confidence in the unbiasedness of findings, particularly when missingness mechanisms are uncertain.