Modern sources of controls in case-control studies

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.

Authors

Institutions

Publication Details

Journal
American Journal of Epidemiology
Published
2024-11-19
DOI
https://doi.org/10.1093/aje/kwae437
Citations
1
Primary Topic
Advanced Causal Inference Techniques
Type
article
Field-Weighted Citation Impact
0.71

Funders

Controls
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article

Modern sources of controls in case-control studies

Michael D. Garber, Jonathan S. Schildcrout, Hailey R. Banack, Robert W. Platt et al.
1 citations
American Journal of Epidemiology
Advanced Causal Inference Techniques
0.71
article

Modern sources of controls in case-control studies

Michael D. Garber, Jonathan S. Schildcrout, Hailey R. Banack, Robert W. Platt, Ellicott C. Matthay, Matthew P. Fox, Xiaojuan Li
article en
1 citations

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.

American Journal of EpidemiologyVol. 194(9)
Boston University (US), 3M (United States) (US), Harvard University (US), Scripps Institution of Oceanography (US), University of Toronto (CA), McGill University Health Centre (CA), University of California San Diego (US), Harvard Pilgrim Health Care (US), Public Health Ontario (CA), McGill University (CA), New York University (US), Vanderbilt University Medical Center (US)
National Cancer Institute
Openalex Percentile: Top 24%
Advanced Causal Inference Techniques
0.71
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