Multiple imputation of incomplete multilevel data using Heckman selection models

Johanna Muñoz*, Orestis Efthimiou, Vincent Audigier, Valentijn M.T. de Jong, Thomas P.A. Debray

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

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Abstract

Missing data is a common problem in medical research, and is commonly addressed using multiple imputation. Although traditional imputation methods allow for valid statistical inference when data are missing at random (MAR), their implementation is problematic when the presence of missingness depends on unobserved variables, that is, the data are missing not at random (MNAR). Unfortunately, this MNAR situation is rather common, in observational studies, registries and other sources of real-world data. While several imputation methods have been proposed for addressing individual studies when data are MNAR, their application and validity in large datasets with multilevel structure remains unclear. We therefore explored the consequence of MNAR data in hierarchical data in-depth, and proposed a novel multilevel imputation method for common missing patterns in clustered datasets. This method is based on the principles of Heckman selection models and adopts a two-stage meta-analysis approach to impute binary and continuous variables that may be outcomes or predictors and that are systematically or sporadically missing. After evaluating the proposed imputation model in simulated scenarios, we illustrate it use in a cross-sectional community survey to estimate the prevalence of malaria parasitemia in children aged 2-10 years in five regions in Uganda.

Original languageEnglish
Pages (from-to)514-533
Number of pages20
JournalStatistics in Medicine
Volume43
Issue number3
DOIs
Publication statusPublished - 10 Feb 2024

Keywords

  • Heckman model
  • IPDMA
  • missing not at random
  • multiple imputation
  • selection models

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