ArticleChemometrics and intelligent laboratory systems : an international journal sponsored by the Chemometrics Society2019
BiMM forest: A random forest method for modeling clustered and longitudinal binary outcomes.
Article in Chemometrics and intelligent laboratory systems : an international journal sponsored by the Chemometrics Society, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
23 citing papers in PubMed.
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- Owner Social Determinants of Health Associated with Exercise Patterns in Golden Retrievers with and Without Cancer.Veterinary sciences · 2026Article
- Machine Learning-Based Prediction of Antepartum Depression Symptoms: A Prospective Cohort Study.International journal of women's health · 2026Article
- Logistic Mixed-Effects Model Analysis With Pseudo-Observations for Estimating Risk Ratios in Clustered Binary Data Analysis.Statistics in medicine · 2025Article
- Explaining Person-by-Item Responses using Person- and Item-Level Predictors via Random Forests and Interpretable Machine Learning in Explanatory Item Response Models.Psychometrika · 2025Article
- OpenClustered: an R package with a benchmark suite of clustered datasets for methodological evaluation and comparison.BMC medical research methodology · 2025Article
- Digital phenotyping in bipolar disorder: Using longitudinal Fitbit data and personalized machine learning to predict mood symptomatology.Acta psychiatrica Scandinavica · 2025Article
- Article
- [Identification of Osteoarthritis Inflamm-Aging Biomarkers by Integrating Bioinformatic Analysis and Machine Learning Strategies and the Clinical Validation].Sichuan da xue xue bao. Yi xue ban = Journal of Sichuan University. Medical science edition · 2024Article
- Do LUTS Predict Mortality? An Analysis Using Random Forest Algorithms.Clinical interventions in aging · 2024Article
- Fixed Effects or Mixed Effects Classifiers? Evidence From Simulated and Archival Data.Educational and psychological measurement · 2023Article
- A review on longitudinal data analysis with random forest.Briefings in bioinformatics · 2023Review
- Alternative stopping rules to limit tree expansion for random forest models.Scientific reports · 2022Article
- Predicting Future Mobility Limitation in Older Adults: A Machine Learning Analysis of Health ABC Study Data.The journals of gerontology. Series A, Biological sciences and medical sciences · 2022Article
- Machine learning and design of experiments with an application to product innovation in the chemical industry.Journal of applied statistics · 2022Article
- Machine learning approaches for the prediction of postoperative complication risk in liver resection patients.BMC medical informatics and decision making · 2021Article
- Prediction With Mixed Effects Models: A Monte Carlo Simulation Study.Educational and psychological measurement · 2021Article
- A random forest method with feature selection for developing medical prediction models with clustered and longitudinal data.Journal of biomedical informatics · 2021Article
- A machine learning approach to predict extreme inactivity in COPD patients using non-activity-related clinical data.PloS one · 2021Article
- A New Random Forest Algorithm Based on Learning Automata.Computational intelligence and neuroscience · 2021Article
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
Clustered binary outcomes and datasets with many predictor variables are frequently encountered in clinical research (e.g. longitudinal studies). Generalized linear mixed models (GLMMs) typically employed for clustered endpoints have challenges for some scenarios, particularly for complex datasets which contain many interactions among predictors and nonlinear predictors of outcome. We propose a new method called Binary Mixed Model (BiMM) forest, which combines random forest and GLMM methodology. BiMM forest offers a flexible and stable method which naturally models interactions among predictors and can be employed in the setting of clustered data. Simulation studies show that BiMM forest achieves similar or superior prediction accuracy compared to standard random forest, GLMMs and its tree counterpart (BiMM tree) for clustered binary outcomes. The method is applied to a real dataset from the Acute Liver Failure Study Group. BiMM forest offers an alternative method for modeling clustered binary outcomes which may be applied in myriad research settings.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.