Multiple imputation of incomplete multilevel data using Heckman selection models Article Swipe
Johanna Muñoz
,
Orestis Efthimiou
,
Vincent Audigier
,
Valentijn M. T. de Jong
,
Thomas P. A. Debray
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1002/sim.9965
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1002/sim.9965
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.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1002/sim.9965
- https://onlinelibrary.wiley.com/doi/pdfdirect/10.1002/sim.9965
- OA Status
- hybrid
- Cited By
- 6
- References
- 33
- Related Works
- 10
- OpenAlex ID
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https://openalex.org/W4317543377Canonical identifier for this work in OpenAlex
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https://doi.org/10.1002/sim.9965Digital Object Identifier
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Multiple imputation of incomplete multilevel data using Heckman selection modelsWork title
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2023Year of publication
- Publication date
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2023-12-11Full publication date if available
- Authors
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Johanna Muñoz, Orestis Efthimiou, Vincent Audigier, Valentijn M. T. de Jong, Thomas P. A. DebrayList of authors in order
- Landing page
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https://doi.org/10.1002/sim.9965Publisher landing page
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https://onlinelibrary.wiley.com/doi/pdfdirect/10.1002/sim.9965Direct link to full text PDF
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YesWhether a free full text is available
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hybridOpen access status per OpenAlex
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https://onlinelibrary.wiley.com/doi/pdfdirect/10.1002/sim.9965Direct OA link when available
- Concepts
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Missing data, Imputation (statistics), Inference, Computer science, Statistics, Econometrics, Data mining, Mathematics, Artificial intelligenceTop concepts (fields/topics) attached by OpenAlex
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6Total citation count in OpenAlex
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2025: 2, 2024: 4Per-year citation counts (last 5 years)
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10Other works algorithmically related by OpenAlex
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