ArticleBMC medical research methodology2020
Multiple imputation by predictive mean matching in cluster-randomized trials.
Article in BMC medical research methodology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 papers.
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Who cites it
32 citing papers in PubMed.
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- Frailty as a predictor of adverse in-hospital outcomes in older patients with Takotsubo cardiomyopathy.Journal of geriatric cardiology : JGC · 2026Article
- Impact of Social Determinants of Health on Utilization of Mechanical Circulatory Support Device in STEMI With Cardiogenic Shock.Journal of the Society for Cardiovascular Angiography & Interventions · 2026Article
- Intra-aortic Balloon Pump Utilization Prior to Coronary Artery Bypass Grafting: Social Determinants of Health.Journal of the Society for Cardiovascular Angiography & Interventions · 2026Article
- How is missing data handled in cluster randomized controlled trials? A review of trials published in the NIHR Journals Library 1997-2024.Clinical trials (London, England) · 2026Review
- An evaluation of the efficacy of single-echo and multi-echo fMRI denoising strategies.Network neuroscience (Cambridge, Mass.) · 2026Article
- Article
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- Impact of Mental Health Disorders in Patients with Myocardial Infarction with Non-obstructive Coronary Arteries: A Propensity Score-Matched Nationwide Analysis.Journal of general internal medicine · 2025Article
- Impact of Mental Health Disorders in Patients with Myocardial Infarction with Non-obstructive Coronary Arteries: A Propensity Score-Matched Nationwide Analysis.Journal of general internal medicine · 2025Article
- Implementation of Intravascular Coronary Lithotripsy in the United States: Social Determinants of Health.JACC. Advances · 2025Article
- Handling Missing Outcome Data in Cluster Randomized Trials With Both Individual- and Cluster-Level Dropout.Statistics in medicine · 2025Article
- Association of dietary intake and whole blood essential trace elements with frailty in older adults: a cross-sectional study.Aging clinical and experimental research · 2025Article
- A Multiple Imputation Workflow for Handling Missing Covariate Data in Pharmacometrics Modeling.CPT: pharmacometrics & systems pharmacology · 2025Article
- A Machine Learning Model for Predicting Intensive Care Unit Admission in Inpatients with COVID-19 Using Clinical Data and Laboratory Biomarkers.Biomedicines · 2025Article
- Association of frailty with outcomes in patients with large vessel occlusion stroke undergoing mechanical thrombectomy.Neuroradiology · 2025Article
- Article
- Predicting re-fracture risk factors in older adult osteoporotic vertebral fractures patients with comorbidities: development and validation of nomogram.Frontiers in medicine · 2025Article
- Machine learning-driven prediction of risk factors for postoperative re-fractures in elderly OVCF patients with underlying diseases: model development and validation.Frontiers in medicine · 2025Article
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3 authors.
Funding
Abstract
backgroundRandom effects regression imputation has been recommended for multiple imputation (MI) in cluster randomized trials (CRTs) because it is congenial to analyses that use random effects regression. This method relies heavily on model assumptions and may not be robust to misspecification of the imputation model. MI by predictive mean matching (PMM) is a semiparametric alternative, but current software for multilevel data relies on imputation models that ignore clustering or use fixed effects for clusters. When used directly for imputation, these two models result in underestimation (ignoring clustering) or overestimation (fixed effects for clusters) of variance estimates.
methodsWe develop MI procedures based on PMM that leverage these opposing estimated biases in the variance estimates in one of three ways: weighting the distance metric (PMM-dist), weighting the average of the final imputed values from two PMM procedures (PMM-avg), or performing a weighted draw from the final imputed values from the two PMM procedures (PMM-draw). We use Monte-Carlo simulations to evaluate our newly proposed methods relative to established MI procedures, focusing on estimation of treatment group means and their variances after MI.
resultsThe proposed PMM procedures reduce the bias in the MI variance estimator relative to established methods when the imputation model is correctly specified, and are generally more robust to model misspecification than even the random effects imputation methods.
conclusionsThe PMM-draw procedure in particular is a promising method for multiply imputing missing data from CRTs that can be readily implemented in existing statistical software.
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