ArticleBehavior research methods2021
Multiple imputation of missing data in multilevel models with the R package mdmb: a flexible sequential modeling approach.
Article in Behavior research methods, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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Who cites it
18 citing papers in PubMed.
- Association Between Extreme Climate Exposures and Bodily Pain: Evidence from the Chinese Health and Retirement Longitudinal Study.Journal of epidemiology and global health · 2026Article
- Multiple imputation of multilevel data with single-level models: A fully conditional specification approach using adjusted group means.Behavior research methods · 2026Article
- Machine Learning Approach to Predict Postoperative Pain and Opioid Usage in Elective Primary Spine Surgery: A Retrospective Study.Journal of pain research · 2026Article
- Multiple imputation and pooling strategies for handling wide-format missing data in latent growth curve modeling.Frontiers in psychology · 2026Article
- Neutrophil Percentage-to-Albumin Ratio as a Predictor of Urinary Tract Infection in Patients with Urinary Stone Disease: Development a Novel User-Friendly Tool.Journal of inflammation research · 2026Article
- Parental Postpartum Depression and Children's Socioemotional Development: The Role of Socioeconomic Inequality.JAACAP open · 2025Article
- The interplay of maternal and paternal postpartum depressive symptoms with children's internalizing and externalizing symptoms from childhood to adolescence: does socioeconomic status matter? A longitudinal cohort study.Journal of child psychology and psychiatry, and allied disciplines · 2025Article
- Random forest analysis and lasso regression outperform traditional methods in identifying missing data auxiliary variables when the MAR mechanism is nonlinear (p.s. Stop using Little's MCAR test).Behavior research methods · 2024Article
- Examining the Impact of Simultaneous Alcohol and Cannabis Use on Alcohol Consumption and Consequences: Protocol for an Observational Ambulatory Assessment Study in Young Adults.JMIR research protocols · 2024Article
- Article
- The relationship between oxidative balance score and erectile dysfunction in the U.S. male adult population.Scientific reports · 2024Article
- Comparing DIC and WAIC for multilevel models with missing data.Behavior research methods · 2024Article
- Are web-based stress management interventions effective as an indirect treatment for depression? An individual participant data meta-analysis of six randomised trials.BMJ mental health · 2024Article
- Joint Imputation of General Data.Journal of survey statistics and methodology · 2024Article
- Stochastic Expectation Maximization Algorithm for Linear Mixed-Effects Model with Interactions in the Presence of Incomplete Data.Entropy (Basel, Switzerland) · 2023Article
- Substantive model compatible multilevel multiple imputation: A joint modeling approach.Statistics in medicine · 2022Article
- Unveiling mechanisms of change in digital interventions for depression: Study protocol for a systematic review and individual participant data meta-analysis.Frontiers in psychiatry · 2022Article
- Article
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3 authors.
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Abstract
Multilevel models often include nonlinear effects, such as random slopes or interaction effects. The estimation of these models can be difficult when the underlying variables contain missing data. Although several methods for handling missing data such as multiple imputation (MI) can be used with multilevel data, conventional methods for multilevel MI often do not properly take the nonlinear associations between the variables into account. In the present paper, we propose a sequential modeling approach based on Bayesian estimation techniques that can be used to handle missing data in a variety of multilevel models that involve nonlinear effects. The main idea of this approach is to decompose the joint distribution of the data into several parts that correspond to the outcome and explanatory variables in the intended analysis, thus generating imputations in a manner that is compatible with the substantive analysis model. In three simulation studies, we evaluate the sequential modeling approach and compare it with conventional as well as other substantive-model-compatible approaches to multilevel MI. We implemented the sequential modeling approach in the R package mdmb and provide a worked example to illustrate its application.
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