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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
  • Boston University
  • McGill University
  • University of California at San Diego
  • Harvard Pilgrim Health Care Institute
  • Vanderbilt University
  • New York University

Research output: Contribution to journalArticlepeer-review

3 Scopus 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.

Original languageEnglish
Pages (from-to)2631-2640
Number of pages10
JournalAmerican Journal of Epidemiology
Volume194
Issue number9
DOIs
StatePublished - Sep 1 2025

Keywords

  • bias
  • case-control studies
  • confounding
  • control selection
  • observational studies

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