Muñoz, Johanna; Efthimiou, Orestis; Audigier, Vincent; de Jong, Valentijn M T; Debray, Thomas P A (2024). Multiple imputation of incomplete multilevel data using Heckman selection models. Statistics in medicine, 43(3), pp. 514-533. Wiley-Blackwell 10.1002/sim.9965
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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.
Item Type: |
Journal Article (Original Article) |
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Division/Institute: |
04 Faculty of Medicine > Medical Education > Institute of General Practice and Primary Care (BIHAM) 04 Faculty of Medicine > Pre-clinic Human Medicine > Institute of Social and Preventive Medicine (ISPM) |
UniBE Contributor: |
Efthimiou, Orestis |
Subjects: |
600 Technology > 610 Medicine & health 300 Social sciences, sociology & anthropology > 360 Social problems & social services |
ISSN: |
0277-6715 |
Publisher: |
Wiley-Blackwell |
Funders: |
[222] Horizon 2020 |
Language: |
English |
Submitter: |
Pubmed Import |
Date Deposited: |
11 Dec 2023 15:31 |
Last Modified: |
16 Jan 2024 12:03 |
Publisher DOI: |
10.1002/sim.9965 |
PubMed ID: |
38073512 |
Uncontrolled Keywords: |
Heckman model IPDMA missing not at random multiple imputation selection models |
BORIS DOI: |
10.48350/190160 |
URI: |
https://boris.unibe.ch/id/eprint/190160 |
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