ArticleThe annals of applied statistics2020
IDENTIFYING MAIN EFFECTS AND INTERACTIONS AMONG EXPOSURES USING GAUSSIAN PROCESSES.
Article in The annals of applied statistics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 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
17 citing papers in PubMed.
- Workflow for Statistical Analysis of Environmental Mixtures.Environmental health perspectives · 2026Review
- Semiparametric discovery and estimation of interaction in mixed exposures using stochastic interventions.Journal of causal inference · 2026Article
- Robust Statistical Approaches to Understanding the Causal Effect of Air Pollution Mixtures.Research report (Health Effects Institute) · 2025Article
- Semi-supervised Bayesian integration of multiple spatial proteomics datasets.PLoS computational biology · 2025Article
- TREATMENT EFFECT HETEROGENEITY AND IMPORTANCE MEASURES FOR MULTIVARIATE CONTINUOUS TREATMENTS.The annals of applied statistics · 2025Article
- Collapsible Kernel Machine Regression for Exposomic Analyses.Statistics in medicine · 2025Article
- INFERRING SYNERGISTIC AND ANTAGONISTIC INTERACTIONS IN MIXTURES OF EXPOSURES.The annals of applied statistics · 2025Article
- Environmental Mixtures Analysis (E-MIX) Workflow and Methods Repository.medRxiv : the preprint server for health sciences · 2024Article
- Power Analysis of Exposure Mixture Studies via Monte Carlo Simulations.Statistics in biosciences · 2024Article
- The single and mixed impacts of cadmium, cobalt, lead, and PAHs on systemic immunity inflammation index in male and female.Frontiers in public health · 2024Article
- A latent functional approach for modeling the effects of multidimensional exposures on disease risk.Statistics in medicine · 2023Article
- Nutritional Modulation of Associations between Prenatal Exposure to Persistent Organic Pollutants and Childhood Obesity: A Prospective Cohort Study.Environmental health perspectives · 2023Article
- Bayesian multiple index models for environmental mixtures.Biometrics · 2023Article
- Spike-and-slab least absolute shrinkage and selection operator generalized additive models and scalable algorithms for high-dimensional data analysis.Statistics in medicine · 2022Article
- CRITICAL WINDOW VARIABLE SELECTION FOR MIXTURES: ESTIMATING THE IMPACT OF MULTIPLE AIR POLLUTANTS ON STILLBIRTH.The annals of applied statistics · 2022Article
- 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
- IDENTIFYING MAIN EFFECTS AND INTERACTIONS AMONG EXPOSURES USING GAUSSIAN PROCESSES.The annals of applied statistics · 2020Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
Abstract
This article is motivated by the problem of studying the joint effect of different chemical exposures on human health outcomes. This is essentially a nonparametric regression problem, with interest being focused not on a black box for prediction but instead on selection of main effects and interactions. For interpretability we decompose the expected health outcome into a linear main effect, pairwise interactions and a nonlinear deviation. Our interest is in model selection for these different components, accounting for uncertainty and addressing nonidentifiability between the linear and nonparametric components of the semiparametric model. We propose a Bayesian approach to inference, placing variable selection priors on the different components, and developing a Markov chain Monte Carlo (MCMC) algorithm. A key component of our approach is the incorporation of a heredity constraint to only include interactions in the presence of main effects, effectively reducing dimensionality of the model search. We adapt a projection approach developed in the spatial statistics literature to enforce identifiability in modeling the nonparametric component using a Gaussian process. We also employ a dimension reduction strategy to sample the nonlinear random effects that aids the mixing of the MCMC algorithm. The proposed MixSelect framework is evaluated using a simulation study, and is illustrated using data from the National Health and Nutrition Examination Survey (NHANES). Code is available on GitHub.
Indexed as
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.