Evidence map›Paper›PMID 40896638›Full record

ArticleThe annals of applied statistics2025

INFERRING SYNERGISTIC AND ANTAGONISTIC INTERACTIONS IN MIXTURES OF EXPOSURES.

Shounak Chattopadhyay, Stephanie M Engel, David Dunson

Abstract read
In one paragraph

Article in The annals of applied statistics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

4 citing papers in PubMed.

  1. Workflow for Statistical Analysis of Environmental Mixtures.Environmental health perspectives · 2026
    Review
  2. Article
  3. Article
  4. Environmental Mixtures Analysis (E-MIX) Workflow and Methods Repository.medRxiv : the preprint server for health sciences · 2024
    Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Shounak ChattopadhyayDepartment of Biostatistics, University of California, Los Angeles.
Stephanie M EngelDepartment of Epidemiology, Gillings School of Global Public Health, University of North Carolina at Chapel Hill.
David DunsonDepartments of Statistical Science & Mathematics, Duke University.

Funding

Reproducibility and Robustness of Dimensionality ReductionR01ES027498 · NIEHS · DUKE UNIVERSITY · PI HERRING, AMY H · 2017 to 2021
$2.9M
Structured nonparametric methods for mixtures of exposuresR01ES028804 · NIEHS · DUKE UNIVERSITY · PI DUNSON, DAVID BRIAN · 2018 to 2021
$2.2M
Improving inferences on health effects of chemical exposuresR01ES035625 · NIEHS · DUKE UNIVERSITY · PI David Brian Dunson · 2023 to 2026
$1.6M
NIEHS NIH HHS R01 ES027498NIEHS NIH HHS R01 ES028804NIEHS NIH HHS R01 ES035625
6 · The paper itself

Abstract

There is abundant interest in assessing the joint effects of multiple exposures on human health. This is often referred to as the mixtures problem in environmental epidemiology and toxicology. Classically, studies have examined the adverse health effects of different chemicals one at a time, but there is concern that certain chemicals may act together to amplify each other's effects. Such amplification is referred to as

Indexed as

Additive modelBayesiandose response surfaceexposure mixturesnonparametric regressionpairwise interactionpositivity constraintsynergy

Identifiers

PMID40896638
PMCPMC12393835

What OpenQuestion holds

Textmetadata
LicenceTDM
Read underepoch 390

Registered trials

None linked

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.