When negative control exposures fail to detect bias

Clicks: 1
ID: 338201
2026
Free PDF
Article Quality & Performance Metrics
Overall Quality
Not rated
Combines reader engagement with the AI quality analysis. This article has not been analysed, so there is no overall score — reader engagement is measured and shown alongside.
AI Quality Assessment
Not analyzed
Readership in this journal

Ranked #378 of 378 articles by views in american journal of epidemiology

Most read Least read

Bar heights use a square-root scale. Only the 120 most-read articles are drawn; the journal has 378 in total.

Mint this article as an NFT
Not yet minted

Create a permanent, verifiable on-chain record of this article on the Scimatic Network. The NFT is held in your Journament account, and you can withdraw it to your own wallet at any time.

5 SUSD one-off · no wallet required
Sign in to mint
Abstract
Abstract Researchers increasingly utilize negative control exposures (NCEs), variables known to have no effect on the outcome, to detect residual confounding. However, an NCE analysis may provide little evidence of confounding despite substantial bias in the estimated exposure effect. We study, analytically and via simulations, how the magnitude and source of exposure-NCE association affect the large-sample NCE coefficient and statistical power of regression-based bias-detection. We show that the power does not always increase with bias, and that stronger confounder-exposure associations can increase bias while reducing the NCE coefficient and power. Furthermore, the correlation between the exposure of interest and the NCE is insufficient to characterize how well the NCE will detect confounding. Additional exposure-NCE common causes that affect the outcome only through the exposure can attenuate the NCE coefficient toward zero or reverse its sign, even when confounding bias remains unchanged. These changes can occur even when the overall exposure-NCE correlation is fixed. Adjusting for such common causes alleviated sign reversal in our simulations but did not uniformly improve power. Negative controls can be a powerful tool to detect unmeasured confounding. Researchers should consider the sources of exposure-NCE association and interpret null or counterintuitive NCE findings with caution.
Reference Key
openalex_W7225086440 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Daniel Nevo, Raanan Raz
Are you one of these authors? Claim your profile
Journal american journal of epidemiology
Year 2026
DOI
10.1093/aje/kwag282
URL
https://doi.org/10.1093/aje/kwag282
Keywords Keywords not found

Citations

No citations found. To add a citation, contact the admin at info@scimatic.org

Comments

No comments yet. Be the first to comment on this article.