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A multiple imputation approach for MNAR mechanisms compatible with Heckman's model.

Jacques-Emmanuel Galimard1,2, Sylvie Chevret1,2,3, Camelia Protopopescu4

  • 1INSERM U1153, Statistic and Epidemiologic Research Center Sorbonne Paris Cité (CRESS), ECSTRA Team, Saint-Louis Hospital, Paris, 75010, France.

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Summary

This study introduces a novel method to handle missing data in clinical research. By extending multiple imputation (MI) with Heckman

Keywords:
Heckman's modelmissing datamissing not at random (MNAR)multiple imputation: chained equationsample selection

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Area of Science:

  • Biostatistics
  • Epidemiology
  • Statistical Modeling

Background:

  • Standard multiple imputation (MI) methods assume data are missing at random (MAR), which is often violated in practice.
  • Missing not at random (MNAR) mechanisms lead to biased inferences with standard MI.
  • Heckman's model effectively addresses MNAR outcomes but is limited to outcomes, not covariates, in clinical settings.

Purpose of the Study:

  • To extend the applicability of multiple imputation (MI) to scenarios with missing not at random (MNAR) data.
  • To integrate Heckman's model within a multiple imputation framework to handle missing covariates and outcomes.
  • To provide a robust statistical approach for handling complex missing data patterns in clinical epidemiology.

Main Methods:

  • Proposed an extension of MI using Heckman's model as the imputation model.
  • Employed a two-step estimation process within the imputation framework.
  • Integrated the approach into a multiple imputation by chained equations (MICE) framework for imputing MAR or MNAR data.

Main Results:

  • The proposed method allows for the imputation of missing data (covariates or outcomes) under specific MNAR mechanisms compatible with Heckman's model.
  • Demonstrated the approach's utility on a real-world dataset from a seasonal influenza randomized trial.
  • Provides a viable solution for biased inferences often encountered with standard MI under MNAR conditions.

Conclusions:

  • The novel MI approach incorporating Heckman's model extends valid statistical inference to data with MNAR mechanisms.
  • This method offers a practical solution for handling missing outcomes and covariates in clinical epidemiology.
  • The technique enhances the robustness of statistical analyses when dealing with complex missing data patterns.