Evidence map›Paper›PMID 31299670›Full record

ArticleEpidemiology (Cambridge, Mass.)2019

Estimating Outcome-Exposure Associations when Exposure Biomarker Detection Limits vary Across Batches.

Jonathan Boss, Bhramar Mukherjee, Kelly K Ferguson, Amira Aker, Akram N Alshawabkeh, José F Cordero, John D Meeker, Sehee Kim

Open access · greenAbstract read
In one paragraph

Article in Epidemiology (Cambridge, Mass.), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.

0numbers the graph read from it
0cells of the map it votes in
22citing papers in PubMed
8.2field-weighted citation impact, top 3% of its field
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

22 citing papers in PubMed, 42 citations in OpenAlex.

  1. Inflammatory Markers in Mother's Own Milk and Infant Stool of Very Low Birthweight Infants.Journal of human lactation : official journal of International Lactation Consultant Association · 2023
    Trial
  2. Article
  3. Nutritional state-dependent changes in subcortical limbic structures in anorexia nervosa are linked to leptin, ghrelin, and neurofilament light.Neuropsychopharmacology : official publication of the American College of Neuropsychopharmacology · 2026
    Article
  4. Article
  5. Article
  6. Article
  7. Article
  8. Frontiers in nutrition · 2025
    Article
  9. Article
  10. Article
  11. Article
  12. Article
  13. Article
  14. Article
  15. Article
  16. Article
  17. Article
  18. Article
  19. Article
  20. Variable selection with multiply-imputed datasets: choosing between stacked and grouped methods.Journal of computational and graphical statistics : a joint publication of American Statistical Association, Institute of Mathematical Statistics, Interface Foundation of North America · 2022
    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

8 authors at 4 institutions in 1 country.

Jonathan BossFrom the Department of Biostatistics, University of Michigan School of Public Health, Ann Arbor, MI.
Bhramar MukherjeeFrom the Department of Biostatistics, University of Michigan School of Public Health, Ann Arbor, MI.
Kelly K FergusonEpidemiology Branch, National Institute of Environmental Health Sciences, Research Triangle Park, Durham, NC.
Amira AkerDepartment of Environmental Health Sciences, University of Michigan School of Public Health, Ann Arbor, MI.
Akram N AlshawabkehDepartment of Civil and Environmental Engineering, Northeastern University College of Engineering, Boston, MA.
José F CorderoDepartment of Epidemiology and Biostatistics, University of Georgia College of Public Health, Athens, GA.
John D MeekerDepartment of Environmental Health Sciences, University of Michigan School of Public Health, Ann Arbor, MI.
Sehee KimFrom the Department of Biostatistics, University of Michigan School of Public Health, Ann Arbor, MI.
University of Michigan–Ann Arbor · USGeorgia College & State University · USNational Institute of Environmental Health Sciences · USNortheastern University · US

Funding

Research Experiences and Training Coordination CoreP42ES017198 · NIEHS · NORTHEASTERN UNIVERSITY · PI PIEPER, KELSEY JANETTE · 2010 to 2024
$38.6M
Environmental Influences on Child Health Outcomes in Puerto Rico (ECHO-PRO)UH3OD023251 · OD · NORTHEASTERN UNIVERSITY · PI Akram N Alshawabkeh · 2018 to 2026
$24.0M
Strategic Vision & Impact on Environmental HealthP30ES017885 · NIEHS · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI John D. Meeker · 2011 to 2026
$21.5M
Environmental Influences on Child Health Outcomes in Puerto Rico (ECHO-PRO)UG3OD023251 · OD · NORTHEASTERN UNIVERSITY · PI ALSHAWABKEH, AKRAM N · 2016 to 2024
$13.0M
LIFECODES pregnancy cohortZIAES103321 · NIEHS · NATIONAL INSTITUTE OF ENVIRONMENTAL HEALTH SCIENCES · PI FERGUSON, KELLY · 2016 to 2025
$6.1M
NIEHS NIH HHS P30 ES017885NIEHS NIH HHS P42 ES017198NIH HHS UG3 OD023251NIH HHS UH3 OD023251
6 · The paper itself

Abstract

Limit of detection (LOD) issues are ubiquitous in exposure assessment. Although there is an extensive literature on modeling exposure data under such imperfect measurement processes, including likelihood-based methods and multiple imputation, the standard practice continues to be naïve single imputation by a constant (e.g., (Equation is included in full-text article.)). In this article, we consider the situation where, due to the practical logistics of data accrual, sampling, and resource constraints, exposure data are analyzed in multiple batches where the LOD and the proportion of censored observations differ across batches. Compounding this problem is the potential for nonrandom assignment of samples to each batch, often driven by enrollment patterns and biosample storage. This issue is particularly important for binary outcome data where batches may have different levels of outcome enrichment. We first consider variants of existing methods to address varying LODs across multiple batches. We then propose a likelihood-based multiple imputation strategy to impute observations that are below the LOD while simultaneously accounting for differential batch assignment. Our simulation study shows that our proposed method has superior estimation properties (i.e., bias, coverage, statistical efficiency) compared to standard alternatives, provided that distributional assumptions are satisfied. Additionally, in most batch assignment configurations, complete-case analysis can be made unbiased by including batch indicator terms in the analysis model, although this strategy is less efficient relative to the proposed method. We illustrate our method by analyzing data from a cohort study in Puerto Rico that is investigating the relation between endocrine disruptor exposures and preterm birth.

Indexed as

Data Interpretation, StatisticalEpidemiologic Research DesignLimit of DetectionModels, StatisticalBiomarkersComputer SimulationEnvironmental ExposureHumansBiomarkers

Identifiers

PMID31299670
PMCPMC6677587
OpenAlexW2957811995

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.