Evidence map›Paper›PMID 21976358›Full record

ArticleStatistics in medicine2012

Sensitivity analysis for interactions under unmeasured confounding.

Tyler J Vanderweele, Bhramar Mukherjee, Jinbo Chen

Abstract read
In one paragraph

Article in Statistics in medicine, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers.

0numbers the graph read from it
0cells of the map it votes in
28citing 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

28 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Evaluation of a Gene-Environment Interaction ofEnvironmental health perspectives · 2022
    Article
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  6. Article
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  16. Review
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  18. Article
  19. Nonparametric Bounds and Sensitivity Analysis of Treatment Effects.Statistical science : a review journal of the Institute of Mathematical Statistics · 2014
    Article
  20. 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.

Tyler J VanderweeleDepartment of Epidemiology, Harvard School of Public Health, 677 Huntington Avenue, Boston, MA 02115, USA. tvanderw@hsph.harvard.edu2552
Bhramar Mukherjee
Jinbo Chen

Funding

Theory and methods for sufficient cause interactionsR01ES017876 · NIEHS · HARVARD SCHOOL OF PUBLIC HEALTH · PI VANDERWEELE, TYLER · 2010 to 2014
$1.2M
Intramural NIH HHSNIEHS NIH HHS R01 ES017876
6 · The paper itself

Abstract

We develop a sensitivity analysis technique to assess the sensitivity of interaction analyses to unmeasured confounding. We give bias formulas for sensitivity analysis for interaction under unmeasured confounding on both additive and multiplicative scales. We provide simplified formulas in the case in which either one of the two factors does not interact with the unmeasured confounder in its effects on the outcome. An interesting consequence of the results is that if the two exposures of interest are independent (e.g., gene-environment independence), even under unmeasured confounding, if the estimate of the interaction is nonzero, then either there is a true interaction between the two factors or there is an interaction between one of the factors and the unmeasured confounder; an interaction must be present in either scenario. We apply the results to two examples drawn from the literature.

Indexed as

BiasData Interpretation, StatisticalModels, StatisticalArsenic PoisoningBody Mass IndexGlutathione TransferaseHumansLung NeoplasmsPrecancerous ConditionsTobacco Smoke PollutionGlutathione TransferaseTobacco Smoke Pollution

Identifiers

PMID21976358
PMCPMC4226658

What OpenQuestion holds

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Registered trials

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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.