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JAMA Guide to Statistics and Methods
March 14, 2017

Logistic Regression Diagnostics: Understanding How Well a Model Predicts Outcomes

Author Affiliations
  • 1Departments of Emergency Medicine and Neurology, University of Michigan, Ann Arbor
  • 2Department of Emergency Medicine, Harbor-UCLA Medical Center, Torrance, California
  • 3Los Angeles Biomedical Research Institute, Torrance, California
  • 4David Geffen School of Medicine at UCLA, Los Angeles, California
JAMA. 2017;317(10):1068-1069. doi:10.1001/jama.2016.20441

In the March 8, 2016, issue of JAMA, Zemek et al1 used logistic regression to develop a clinical risk score for identifying which pediatric patients with concussion will experience prolonged postconcussion symptoms (PPCS). The authors prospectively recorded the initial values of 46 potential predictor variables, or risk factors—selected based on expert opinion and previous research—in a cohort of patients and then followed those patients to determine who developed the primary outcome of PPCS. In the first part of the study, the authors created a logistic regression model to estimate the probability of PPCS using a subset of the variables; in the second part of the study, a separate set of data was used to assess the validity of the model, with the degree of success quantified using regression model diagnostics. The rationale for using logistic regression to develop predictive models was summarized in an earlier JAMA Guide to Statistics and Methods article.2 In this article, we discuss how well a model performs once it is defined.

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