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Multiple imputation of incomplete multilevel data using Heckman selection models.

Johanna Muñoz1, Orestis Efthimiou2,3, Vincent Audigier4

  • 1Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands.

Statistics in Medicine
|December 11, 2023
PubMed
Summary
This summary is machine-generated.

Missing data in medical research, especially when missing not at random (MNAR), poses challenges. A new multilevel imputation method, based on Heckman selection models, effectively handles missing data in complex, hierarchical datasets.

Keywords:
Heckman modelIPDMAmissing not at randommultiple imputationselection models

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Area of Science:

  • Biostatistics
  • Epidemiology
  • Medical Informatics

Background:

  • Missing data is prevalent in medical research, particularly in real-world data sources like registries.
  • Traditional multiple imputation methods are valid for data missing at random (MAR) but struggle with data missing not at random (MNAR).
  • The application of MNAR imputation methods in large, multilevel datasets remains unclear.

Conclusions:

  • The novel multilevel imputation method offers a robust solution for addressing MNAR data in complex, clustered medical datasets.
  • This approach enhances the validity of statistical inference in the presence of unobserved missingness mechanisms.
  • The method is applicable to various data types (binary, continuous) and can be used for outcomes or predictors.