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Journey across epidemiology’s third variables: an anesthesiologist’s guide for successfully navigating confounding, mediation, and effect modification
  1. Joshua Levy1,2,3,
  2. Rebecca Lebeaux1,2,
  3. Brock Christensen1,4,5,
  4. Tor Tosteson6,7 and
  5. Yvon Bryan8
  1. 1Department of Epidemiology, Dartmouth College Geisel School of Medicine, Hanover, New Hampshire, USA
  2. 2Program in Quantitative Biomedical Sciences, Dartmouth College Geisel School of Medicine, Hanover, NH, USA
  3. 3Department of Pathology and Laboratory Medicine, Dartmouth Hitchcock Medical Center, Lebanon, NH, USA
  4. 4Department of Molecular and Systems Biology, Dartmouth College Geisel School of Medicine, Hanover, NH, USA
  5. 5Department of Community and Family Medicine, Dartmouth College Geisel School of Medicine, Hanover, NH, USA
  6. 6Department of Biomedical Data Science, Dartmouth College Geisel School of Medicine, Hanover, New Hampshire, USA
  7. 7The Dartmouth Institute, Dartmouth College Geisel School of Medicine, Hanover, NH, USA
  8. 8Department of Anesthesiology, Dartmouth-Hitchcock Medical Center, Lebanon, New Hampshire, USA
  1. Correspondence to Joshua Levy, Department of Epidemiology, Dartmouth College Geisel School of Medicine, Hanover NH 03755, USA; joshua.j.levy.gr{at}dartmouth.edu

Abstract

Observational clinical research studies aim to assess which exposures (treatments or other factors; independent variable) affect patient outcomes (dependent variable). These exposures include medical interventions in situations where clinical trials are not possible or prior to their conduct and completion. However, the assessment of the relationship between exposures and outcomes is not straightforward, as other variables may need to be considered prior to reaching valid conclusions. Here, we present three hypothetical scenarios in regional anesthesia to review the epidemiological concepts of confounding, mediation, and effect modification. Understanding these concepts is critical for assessing the design, analysis, and interpretation of clinical studies. These terms may be confusing to anesthesiologists and researchers alike, where such confusion could affect the conclusions of a clinical study, mislead the target audience, and ultimately impact patient health.

  • epidemiology
  • education
  • outcome assessment
  • health care
  • methods

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Footnotes

  • Contributors All authors discussed the contents of the manuscript and contributed to the final version of the manuscript.

  • Funding RL was supported by NIH T32AI007519. JL was supported through the Burroughs Wellcome, Big Data in the Life Sciences training grant at Dartmouth. TT and BC received support from NIH/NCATS UL1TR001086. BC received support from NIH R01CA216265, R01DE022772, and P20GM104416.

  • Competing interests None declared.

  • Provenance and peer review Not commissioned; externally peer reviewed.

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