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JAMA Guide to Statistics and Methods
January 24, 2019

Using the E-Value to Assess the Potential Effect of Unmeasured Confounding in Observational Studies

Author Affiliations
  • 1Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, Massachusetts
  • 2Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, Massachusetts
  • 3Kaiser Permanente Washington Health Research Institute, Seattle, Washington
  • 4Department of Medicine, University of Washington, Seattle
JAMA. 2019;321(6):602-603. doi:10.1001/jama.2018.21554

Randomized trials serve as the standard for comparative studies of treatment effects. In many settings, it may not be feasible or ethical to conduct a randomized study,1 and researchers may pursue observational studies to better understand clinical outcomes. A central limitation of observational studies is the potential for confounding bias that arises because treatment assignment is not random. Thus, the observed associations may be attributable to differences other than the treatment being investigated and causality cannot be assumed.

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