Evidence map›Paper›PMID 38755683›Full record

ArticleEnvironmental health : a global access science source2024

Accommodating detection limits of multiple exposures in environmental mixture analyses: an overview of statistical approaches.

Myeonggyun Lee, Abhisek Saha, Rajeshwari Sundaram, Paul S Albert, Shanshan Zhao

Abstract read
In one paragraph

Article in Environmental health : a global access science source, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.

0numbers the graph read from it
0cells of the map it votes in
14citing 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

14 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Workflow for Statistical Analysis of Environmental Mixtures.Environmental health perspectives · 2026
    Review
  5. Article
  6. Article
  7. Article
  8. Article
  9. Article
  10. Article
  11. Article
  12. Article
  13. Article
  14. 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

5 authors.

Myeonggyun LeeBiostatistics and Computational Biology Branch, National Institute of Environmental Health Sciences, National Institutes of Health, Durham, NC, USA.
Abhisek SahaBiostatistics and Bioinformatics Branch, Division of Population Health Research, Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, Bethesda, MD, USA.
Rajeshwari SundaramBiostatistics and Bioinformatics Branch, Division of Population Health Research, Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, Bethesda, MD, USA.
Paul S AlbertBiostatistics Branch, Division Cancer Epidemiology and GeneticsBiostatistics Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA.
Shanshan ZhaoBiostatistics and Computational Biology Branch, National Institute of Environmental Health Sciences, National Institutes of Health, Durham, NC, USA. shanshan.zhao@nih.gov.

Funding

Statistical Methods in EpidemiologyZIAES103307 · NIEHS · NATIONAL INSTITUTE OF ENVIRONMENTAL HEALTH SCIENCES · PI ZHAO, SHANSHAN · 2015 to 2025
$8.5M
Application of Statistical Methods in Epidemiology StudiesZIAES103308 · NIEHS · NATIONAL INSTITUTE OF ENVIRONMENTAL HEALTH SCIENCES · PI ZHAO, SHANSHAN · 2015 to 2025
$2.2M
Intramural NIH HHS ZIA ES103307NIEHS NIH HHS ZIA ES103307
6 · The paper itself

Abstract

backgroundIdentifying the impact of environmental mixtures on human health is an important topic. However, such studies face challenges when exposure measurements lie below limit of detection (LOD). While various approaches for accommodating a single exposure subject to LOD have been used, their impact on mixture analysis has not been thoroughly investigated. Our study aims to understand the impact of five popular LOD accommodation approaches on mixture analysis results with multiple exposures subject to LOD, including omitting subjects with any exposures below LOD (complete case analysis); single imputations by LOD/

methodsIn extensive simulation studies with high-dimensional and highly correlated exposures and a continuous health outcome, we examined the performance of each LOD approach on three mixture analysis methods: elastic net regression, weighted quantile sum regression (WQS) and Bayesian kernel machine regression (BKMR). We further analyzed data from the National Health and Nutrition Examination Survey (NHANES) on how persistent organic pollutants (POPs) influenced leukocyte telomere length (LTL).

resultsComplete case analysis was inefficient and could result in severe bias for some mixture methods. Imputation by LOD/

conclusionsOur study favored using truncated MI and censored AFT models to accommodate values below LOD for the stability of downstream mixture analysis.

Indexed as

Environmental ExposureEnvironmental PollutantsEnvironmental MonitoringHumansLimit of DetectionModels, StatisticalNutrition SurveysEnvironmental PollutantsDetection limitsEnvironmental mixturesImputationLeft-censored exposures

Identifiers

PMID38755683
PMCPMC11097582

What Socratic holds

Textmetadata
LicenceCC BY
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 Socratic graph.