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JAMA Guide to Statistics and Methods
March 8, 2019

Marginal Effects—Quantifying the Effect of Changes in Risk Factors in Logistic Regression Models

Author Affiliations
  • 1Department of Health Management and Policy, Department of Economics, University of Michigan, Ann Arbor
  • 2National Bureau of Economic Research, Cambridge, Massachusetts
  • 3Division of Health Policy and Management, School of Public Health, University of Minnesota, Minneapolis
  • 4Durham Center of Innovation to Accelerate Discovery and Practice Transformation (ADAPT), Durham Veterans Affairs Health Care System, Durham, North Carolina
  • 5Department of Population Health Sciences, Duke University School of Medicine, Durham, North Carolina
  • 6Division of General Internal Medicine, Department of Medicine, Duke University School of Medicine, Durham, North Carolina
JAMA. 2019;321(13):1304-1305. doi:10.1001/jama.2019.1954

Marginal effects can be used to express how the predicted probability of a binary outcome changes with a change in a risk factor. For example, how does 1-year mortality risk change with a 1-year increase in age or for a patient with diabetes compared with a patient without diabetes? This approach can make the results more easily understood. Marginal effects often are reported with logistic regression analyses to communicate and quantify the incremental risk associated with each factor.1,2