ArticleStatistics in medicine2021
Bayesian variable selection for understanding mixtures in environmental exposures.
Article in Statistics in medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
The trial behind it
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
11 citing papers in PubMed.
- Facilitating Heterogeneous Effect Estimation via Statistically Efficient Categorical Modifiers.Journal of the American Statistical Association · 2026Article
- Workflow for Statistical Analysis of Environmental Mixtures.Environmental health perspectives · 2026Review
- Environmental Mixtures Analysis (E-MIX) Workflow and Methods Repository.medRxiv : the preprint server for health sciences · 2024Article
- Spatial Variability in Relationships between Early Childhood Lead Exposure and Standardized Test Scores in Fourth Grade North Carolina Public School Students (2013-2016).Environmental health perspectives · 2024Article
- Regression with race-modifiers: towards equity and interpretability.medRxiv : the preprint server for health sciences · 2024Article
- Combined Exposures and Mixtures Research: An Enduring NIEHS Priority.Environmental health perspectives · 2024Article
- Regression with race-modifiers: towards equity and interpretability.Research square · 2024Article
- Subset selection for linear mixed models.Biometrics · 2023Article
- Powering Research through Innovative Methods for Mixtures in Epidemiology (PRIME) Program: Novel and Expanded Statistical Methods.International journal of environmental research and public health · 2022Review
- Fast, Optimal, and Targeted Predictions using Parametrized Decision Analysis.Journal of the American Statistical Association · 2022Article
- Bayesian subset selection and variable importance for interpretable prediction and classification.Journal of machine learning research : JMLRArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
Abstract
Social and environmental stressors are crucial factors in child development. However, there exists a multitude of measurable social and environmental factors-the effects of which may be cumulative, interactive, or null. Using a comprehensive cohort of children in North Carolina, we study the impact of social and environmental variables on 4th end-of-grade exam scores in reading and mathematics. To identify the essential factors that predict these educational outcomes, we design new tools for Bayesian linear variable selection using decision analysis. We extract a predictive optimal subset of explanatory variables by coupling a loss function with a novel model-based penalization scheme, which leads to coherent Bayesian decision analysis and empirically improves variable selection, estimation, and prediction on simulated data. The Bayesian linear model propagates uncertainty quantification to all predictive evaluations, which is important for interpretable and robust model comparisons. These predictive comparisons are conducted out-of-sample with a customized approximation algorithm that avoids computationally intensive model refitting. We apply our variable selection techniques to identify the joint collection of social and environmental stressors-and their interactions-that offer clear and quantifiable improvements in prediction of reading and mathematics exam scores.
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Identifiers
What OpenQuestion holds
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.