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JAMA Guide to Statistics and Methods
August 2, 2016

Logistic RegressionRelating Patient Characteristics to Outcomes

Author Affiliations
  • 1Department of Emergency Medicine, Harbor-UCLA Medical Center, Torrance, California
  • 2Los Angeles Biomedical Research Institute, Torrance, California
  • 3David Geffen School of Medicine, University of California, Los Angeles
  • 4Department of Emergency Medicine, University of Michigan, Ann Arbor
  • 5Department of Neurology, University of Michigan, Ann Arbor

Copyright 2016 American Medical Association. All Rights Reserved. Applicable FARS/DFARS Restrictions Apply to Government Use.

JAMA. 2016;316(5):533-534. doi:10.1001/jama.2016.7653

In a recent issue of JAMA, Seymour et al1 presented a new method for estimating the probability of a patient dying of sepsis using information on the patient’s respiratory rate, systolic blood pressure, and altered mentation. The method used these clinical characteristics—called “predictor” or explanatory or independent variables—to estimate the likelihood of a patient having an outcome of interest, called the dependent variable. To determine the best way to use these clinical characteristics, the authors used logistic regression, a common statistical method for quantifying the relationship between patient characteristics and clinical outcomes.2

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