ArticleStatistics in medicine2017
A comparison of existing methods for multiple imputation in individual participant data meta-analysis.
Article in Statistics in medicine, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 6 of them syntheses that pooled it.
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Who cites it
16 citing papers in PubMed, 6 syntheses or guidelines pooled it.
- Individual Participant Data Meta-Analysis Identifies Risk Factors for Acute and Persistent Posttraumatic Stress Disorder and Depression Symptoms Following Trauma.Depression and anxiety · 2026Pooled it
- Systematically missing data in causally interpretable meta-analysis.Biostatistics (Oxford, England) · 2024Pooled it
- Pooled it
- Addressing Systematic Missing Data in the Context of Causally Interpretable Meta-analysis.Prevention science : the official journal of the Society for Prevention Research · 2023Pooled it
- Pooled it
- Causally Interpretable Meta-analysis: Application in Adolescent HIV Prevention.Prevention science : the official journal of the Society for Prevention Research · 2022Pooled it
- Structured missingness in biomedical research.Communications AI & computing · 2026Article
- A model-agnostic framework for dataset-specific selection of missing value imputation methods in pain-related numerical data.Canadian journal of pain = Revue canadienne de la douleur · 2026Article
- Hierarchical imputation of categorical variables in the presence of systematically and sporadically missing data.Research synthesis methods · 2025Article
- Multiple imputation for systematically missing effect modifiers in individual participant data meta-analysis.Statistical methods in medical research · 2025Article
- Assessment of heterogeneity in an individual participant data meta-analysis of prediction models: An overview and illustration.Statistics in medicine · 2019Article
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- Evidence synthesis in prognosis research.Diagnostic and prognostic research · 2019Article
- Measurement error and misclassification in electronic medical records: methods to mitigate bias.Current epidemiology reports · 2018Article
- A comparison of one-stage vs two-stage individual patient data meta-analysis methods: A simulation study.Research synthesis methods · 2018Article
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Authors and funding
2 authors.
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Abstract
Multiple imputation is a popular method for addressing missing data, but its implementation is difficult when data have a multilevel structure and one or more variables are systematically missing. This systematic missing data pattern may commonly occur in meta-analysis of individual participant data, where some variables are never observed in some studies, but are present in other hierarchical data settings. In these cases, valid imputation must account for both relationships between variables and correlation within studies. Proposed methods for multilevel imputation include specifying a full joint model and multiple imputation with chained equations (MICE). While MICE is attractive for its ease of implementation, there is little existing work describing conditions under which this is a valid alternative to specifying the full joint model. We present results showing that for multilevel normal models, MICE is rarely exactly equivalent to joint model imputation. Through a simulation study and an example using data from a traumatic brain injury study, we found that in spite of theoretical differences, MICE imputations often produce results similar to those obtained using the joint model. We also assess the influence of prior distributions in MICE imputation methods and find that when missingness is high, prior choices in MICE models tend to affect estimation of across-study variability more than compatibility of conditional likelihoods. Copyright © 2017 John Wiley & Sons, Ltd.
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