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JAMA Guide to Statistics and Methods
May 2, 2019

Using Instrumental Variables to Address Bias From Unobserved Confounders

Author Affiliations
  • 1Center of Innovation to Accelerate Discovery & Practice Transformation, Durham Veterans Affairs Medical Center, Durham, North Carolina
  • 2Department of Population Health Sciences, Duke University School of Medicine, Durham, North Carolina
  • 3Division of General Internal Medicine, Department of Medicine, Duke University School of Medicine, Durham, North Carolina
  • 4Department of Epidemiology, University of North Carolina at Chapel Hill Gillings School of Global Public Health
JAMA. 2019;321(21):2124-2125. doi:10.1001/jama.2019.5646

Randomized clinical trials are considered the most reliable source of evidence for the effects of medical interventions, but nonexperimental studies are often used to assess the effectiveness of treatments as they are used in actual clinical practice. In nonexperimental studies, treatment groups may differ by important patient characteristics, such as disease severity, frailty, cognitive function, vulnerability to adverse effects, and ability to pay.1 While statistical adjustment can account for imbalances in observed characteristics between groups, observed imbalances are concerning because they suggest that unobserved differences may also exist. Unobserved patient characteristics that influence both treatment and the outcomes result in “unobserved confounding,” a bias that cannot be removed using standard statistical adjustment.1

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