Evidence map›Paper›PMID 25956004›Full record

ArticleEnvironmental health perspectives2015

Biased Exposure-Health Effect Estimates from Selection in Cohort Studies: Are Environmental Studies at Particular Risk?

Marc G Weisskopf, David Sparrow, Howard Hu, Melinda C Power

Abstract read
In one paragraph

Article in Environmental health perspectives, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 46 papers, 3 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
46citing papers in PubMed, 3 pooled it
–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

46 citing papers in PubMed, 3 syntheses or guidelines pooled it.

  1. Pooled it
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  20. The Association Between Parental Age and Autism-Related Outcomes in Children at High Familial Risk for Autism.Autism research : official journal of the International Society for Autism Research · 2020
    Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

4 authors.

Marc G WeisskopfDepartment of Epidemiology and Department of Environmental Health, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA.
David Sparrow
Howard Hu
Melinda C Power

Funding

Translational Research Support CoreP30ES000002 · NIEHS · HARVARD UNIVERSITY (SCH OF PUBLIC HLTH) · PI SUSAN A KORRICK · 1985 to 2026
$44.6M
Research Training in Age-Related Cognitive DisordersT32AG027668 · NIA · JOHNS HOPKINS UNIVERSITY · PI MARILYN S. ALBERT · 2007 to 2026
$6.3M
LEAD-GENE INTERACTIONS AND CONGNITIONR01ES005257 · NIEHS · BRIGHAM AND WOMEN'S HOSPITAL · PI WEISSKOPF, MARC G · 1991 to 2007
$4.2M
NIA NIH HHS T32 AG027668NIEHS NIH HHS ES-00002NIEHS NIH HHS P30 ES000002NIEHS NIH HHS R01 ES005257NIEHS NIH HHS R01-ES05257
6 · The paper itself

Abstract

backgroundThe process of creating a cohort or cohort substudy may induce misleading exposure-health effect associations through collider stratification bias (i.e., selection bias) or bias due to conditioning on an intermediate. Studies of environmental risk factors may be at particular risk.

objectivesWe aimed to demonstrate how such biases of the exposure-health effect association arise and how one may mitigate them.

methodsWe used directed acyclic graphs and the example of bone lead and mortality (all-cause, cardiovascular, and ischemic heart disease) among 835 white men in the Normative Aging Study (NAS) to illustrate potential bias related to recruitment into the NAS and the bone lead substudy. We then applied methods (adjustment, restriction, and inverse probability of attrition weighting) to mitigate these biases in analyses using Cox proportional hazards models to estimate adjusted hazard ratios (HRs) and 95% confidence intervals (CIs).

resultsAnalyses adjusted for age at bone lead measurement, smoking, and education among all men found HRs (95% CI) for the highest versus lowest tertile of patella lead of 1.34 (0.90, 2.00), 1.46 (0.86, 2.48), and 2.01 (0.86, 4.68) for all-cause, cardiovascular, and ischemic heart disease mortality, respectively. After applying methods to mitigate the biases, the HR (95% CI) among the 637 men analyzed were 1.86 (1.12, 3.09), 2.47 (1.23, 4.96), and 5.20 (1.61, 16.8), respectively.

conclusionsCareful attention to the underlying structure of the observed data is critical to identifying potential biases and methods to mitigate them. Understanding factors that influence initial study participation and study loss to follow-up is critical. Recruitment of population-based samples and enrolling participants at a younger age, before the potential onset of exposure-related health effects, can help reduce these potential pitfalls. CITATION: Weisskopf MG, Sparrow D, Hu H, Power MC. 2015. Biased exposure-health effect estimates from selection in cohort studies: are environmental studies at particular risk? Environ Health Perspect 123:1113-1122; http://dx.doi.org/10.1289/ehp.1408888.

Indexed as

BiasCohort StudiesAgedAged, 80 and overBone and BonesCardiovascular DiseasesEnvironmental ExposureHumansLeadMaleMortalityProportional Hazards ModelsLead

Identifiers

PMID25956004
PMCPMC4629739

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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.