ArticleAmerican journal of epidemiology2021
Addressing Measurement Error in Random Forests Using Quantitative Bias Analysis.
Article in American journal of epidemiology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed.
- Evaluating Biomedical Feature Fusion on Machine Learning's Predictability and Interpretability of COVID-19 Severity Types: Model Development, Interpretation, and Validation.JMIR formative research · 2026Article
- Identifying potential action points for reducing kinesiophobia among atrial fibrillation patients: a network and DAG analysis.Quality of life research : an international journal of quality of life aspects of treatment, care and rehabilitation · 2025Article
- Predictive models of miscarriage on the basis of data from a preconception cohort study.Fertility and sterility · 2024Article
- Factors associated with infant sex and preterm birth status for selected birth defects from the National Birth Defects Prevention Study, 1997-2011.Birth defects research · 2024Article
- Article
- Deep Survival Analysis With Clinical Variables for COVID-19.IEEE journal of translational engineering in health and medicine · 2023Article
- A general algorithm for error-in-variables regression modelling using Monte Carlo expectation maximization.PloS one · 2023Article
- Predictive models of pregnancy based on data from a preconception cohort study.Human reproduction (Oxford, England) · 2022Article
- Timing errors and temporal uncertainty in clinical databases-A narrative review.Frontiers in digital health · 2022Review
- Predicting Sex-Specific Nonfatal Suicide Attempt Risk Using Machine Learning and Data From Danish National Registries.American journal of epidemiology · 2021Article
- Suicide prediction among men and women with depression: A population-based study.Journal of psychiatric research · 2021Article
- Jiang et al. Respond to "Quantitative Bias Analysis".American journal of epidemiology · 2021Article
- Using machine learning to predict suicide in the 30 days after discharge from psychiatric hospital in Denmark.The British journal of psychiatry : the journal of mental science · 2021Article
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Abstract
Although variables are often measured with error, the impact of measurement error on machine-learning predictions is seldom quantified. The purpose of this study was to assess the impact of measurement error on the performance of random-forest models and variable importance. First, we assessed the impact of misclassification (i.e., measurement error of categorical variables) of predictors on random-forest model performance (e.g., accuracy, sensitivity) and variable importance (mean decrease in accuracy) using data from the National Comorbidity Survey Replication (2001-2003). Second, we created simulated data sets in which we knew the true model performance and variable importance measures and could verify that quantitative bias analysis was recovering the truth in misclassified versions of the data sets. Our findings showed that measurement error in the data used to construct random forests can distort model performance and variable importance measures and that bias analysis can recover the correct results. This study highlights the utility of applying quantitative bias analysis in machine learning to quantify the impact of measurement error on study results.
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