ArticleBehavior research methods2024
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).
Article in Behavior research methods, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Tutorial: Using Random Forest Analysis to Identify Auxiliary Variables of Missing Data.Prevention science : the official journal of the Society for Prevention Research · 2026Article
- A machine-learning-assisted logistic regression model for predicting post-operative delirium in older adults undergoing total knee arthroplasty.Frontiers in neurology · 2026Article
- Universal versus risk- factor-based screening for gestational diabetes mellitus: findings from a large-scale cohort study.Frontiers in endocrinology · 2026Article
- Risk factors for progression to bronchitis or pneumonia in hospitalized children with human rhinovirus infection: a retrospective cohort study using machine learning.Frontiers in pediatrics · 2026Article
- The pan-immune-inflammation value predicts stroke-associated pneumonia and poor outcome in spontaneous intracerebral hemorrhage: a machine learning approach.Frontiers in neurology · 2026Article
- A Change Would Do You Good…or Would It? The Role of Emotion Variability in Female Adolescents' Depressive Symptoms.Research on child and adolescent psychopathology · 2025Article
- Random survival forest model in patients with epithelial ovarian cancer: a study based on SEER database and single center data.American journal of cancer research · 2025Article
- Development and validation of nomogram models for predicting immune-related adverse events in recurrent and metastatic nasopharyngeal carcinoma patients treated with PD-L1 inhibitors.Frontiers in oncology · 2025Article
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Authors and funding
3 authors.
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Abstract
The selection of auxiliary variables is an important first step in appropriately implementing missing data methods such as full information maximum likelihood (FIML) estimation or multiple imputation. However, practical guidelines and statistical tests for selecting useful auxiliary variables are somewhat lacking, leading to potentially biased estimates. We propose the use of random forest analysis and lasso regression as alternative methods to select auxiliary variables, particularly in situations in which the missing data pattern is nonlinear or otherwise complex (i.e., interactive relationships between variables and missingness). Monte Carlo simulations demonstrate the effectiveness of random forest analysis and lasso regression compared to traditional methods (t-tests, Little's MCAR test, logistic regressions), in terms of both selecting auxiliary variables and the performance of said auxiliary variables when incorporated in an analysis with missing data. Both techniques outperformed traditional methods, providing a promising direction for improvement of practical methods for handling missing data in statistical analyses.
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Registered trials
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