Evidence map›Paper›PMID 39586815›Full record

ArticleAmerican journal of epidemiology2025

Modern sources of controls in case-control studies.

Hailey R Banack, Matthew P Fox, Robert W Platt, Michael D Garber, Xiaojuan Li, Jonathan Schildcrout, Ellicott C Matthay

Abstract read
In one paragraph

Article in American journal of epidemiology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Review
  2. Article
  3. When air gets heavy: joint PMFrontiers in public health · 2026
    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

7 authors.

Hailey R BanackEpidemiology Division, Dalla Lana School of Public Health, University of Toronto, Toronto, ON M5T 3M7, Canada.
Matthew P FoxDepartments of Epidemiology and of Global Health, Boston University School of Public Health, Boston University, Boston, MA 02118, United States.
Robert W PlattDepartment of Epidemiology, Biostatistics, and Occupational Health, McGill University, Montreal, QC H3A 1Y7, Canada.ORCID 0000-0002-5981-8443
Michael D GarberScripps Institution of Oceanography, UC San Diego, San Diego, CA 92037, United States.ORCID 0000-0003-0122-5634
Xiaojuan LiDepartment of Population Medicine, Harvard Medical School and Harvard Pilgrim Health Care Institute, Boston, MA 02215, United States.
Jonathan SchildcroutDepartment of Biostatistics, Vanderbilt University Medical Center, Nashville, TN 37203, United States.
Ellicott C MatthayCenter for Opioid Epidemiology and Policy, Division of Epidemiology, Department of Population Health, New York University Grossman School of Medicine, New York, NY 10016, United States.ORCID 0000-0003-4535-8252

Funding

Outcome Dependent Sampling Studies of Longitudinal Data: Design and AnalysisR01HL094786 · NHLBI · VANDERBILT UNIVERSITY MEDICAL CENTER · PI Jonathan Scott Schildcrout · 2009 to 2026
$6.1M
Research Continuity and Retention Supplement: Optimizing care for older adults in the new treatment era for type 2 diabetes and heart failureK01AG073651 · NIA · HARVARD PILGRIM HEALTH CARE, INC. · PI Xiaojuan Li · 2022 to 2026
$700k
NHLBI NIH HHS R01 HL094786NIA NIH HHS K01 AG073651
6 · The paper itself

Abstract

In 1992, Wacholder et al. developed a theoretical framework for case-control studies to minimize bias in control selection. They described 3 comparability principles (study base, deconfounding, and comparable accuracy) to reduce the potential for selection bias, confounding, and information bias in case-control studies. Wacholder et al. explained how these principles apply to traditional sources of control participants for case-control studies, including population controls, hospital controls, controls from a medical practice, friend or relative controls, and deceased controls. The goal of the present article is to extend this seminal work on case-control studies by providing a modern perspective on sources of control participants. Today, there are many more potential sources of control participants s for case-control studies than there were in the 1990s. This is due to technological advances in computing power, internet access, and availability of "big data" resources. These advances have vastly expanded the quantity and diversity of data available for case-control studies. We discuss control selection from electronic health records, health insurance claims databases, publicly available online data sources, and social media-based data. We focus on practical considerations for unbiased control selection, emphasizing the strengths and weaknesses of each modern source of controls for case-control studies.

Indexed as

Case-Control StudiesControl GroupsDatabases, FactualElectronic Health RecordsHumansResearch DesignSelection Biasbiascase-control studiesconfoundingcontrol selectionobservational studies

Identifiers

PMID39586815
PMCPMC12409134

What OpenQuestion holds

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LicenceCC BY-NC
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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.