Original Contributions| Volume 63, ISSUE 5, P683-691, November 2022

Primer on Logistic Regression for Emergency Care Researchers



      Logistic regression plays a fundamental role in the production of decision rules, risk assessment, and in establishing cause and effect relationships. This primer is aimed at novice researchers with minimal statistical expertise.


      Introduce the logit equation and provide a hands-on example to facilitate understanding of its benefits and limitations.


      This primer reviews the mathematical basis of a logit equation by comparing and contrasting it with the simple straight-line (linear) equation. After gaining an understanding of the meaning of beta coefficients, readers are encouraged to download a free statistical program and database to produce a logistic regression analysis. Using this example, the narrative then discusses commonly used methods to describe model fitness, including the C-statistic, chi square, Akaike and Bayesian Information Criteria, McFadden's pseudo R2, and the Hosmer-Lemeshow test. The authors provide a how-to discussion for variable selection and estimate of sample size. However, logistic regression alone can seldom establish causal inference without further steps to explore the often complex relationship amongst variables and outcomes, such as with the use of a directed acyclic graphs. We present key elements that generally should be considered when appraising an article that uses logistic regression. This primer provides a basic understanding of the theory, hands-on construction, model analysis, and limitations of logistic regression in emergency care research.


      Logistic regression can provide information about the association of independent variables with important clinical outcomes, which can be the first step to show predictiveness or causation of variables on the outcomes of interest. © 2022 Elsevier Inc.


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      1. Cramer JS. The origins of logistic regression (December 2002). Tinbergen Institute Working Paper No. 2002-119/4. Available at: Accessed October 6, 2022.

        • Kline JA
        • Camargo Jr, CA
        • Courtney DM
        • et al.
        Clinical prediction rule for SARS-CoV-2 infection from 116 U.S. emergency departments 2-22-2021.
        PLoS One. 2021; 16e0248438
        • Feinstein AR.
        Clinical Judgment” revisited: the distraction of quantitative models.
        Ann Intern Med. 1994; 120: 799-805
        • Alba AC
        • Agoritsas T
        • Walsh M
        • et al.
        Discrimination and calibration of clinical prediction models: users' guides to the medical literature.
        JAMA. 2017; 318: 1377-1384
        • Bossuyt PM
        • Reitsma JB
        • Linnet K
        • Moons KG.
        Beyond diagnostic accuracy: the clinical utility of diagnostic tests.
        Clin Chem. 2012; 58: 1636-1643
        • Van Calster B
        • McLernon DJ
        • van Smeden M
        • Wynants L
        • Steyerberg EW.
        Calibration: the Achilles heel of predictive analytics.
        BMC Med. 2019; 17: 230
        • Moons KG
        • Altman DG
        • Reitsma JB
        • et al.
        Transparent Reporting of a multivariable prediction model for Individual Prognosis or Diagnosis (TRIPOD): explanation and elaboration.
        Ann Intern Med. 2015; 162: W1-73
        • Peduzzi P
        • Concato J
        • Kemper E
        • Holford TR
        • Feinstein AR.
        A simulation study of the number of events per variable in logistic regression analysis.
        J Clin Epidemiol. 1996; 49: 1373-1379
        • van Smeden M
        • de Groot JA
        • Moons KG
        • et al.
        No rationale for 1 variable per 10 events criterion for binary logistic regression analysis.
        BMC Med Res Methodol. 2016; 16: 163
        • Corraini P
        • Olsen M
        • Pedersen L
        • Dekkers OM
        • Vandenbroucke JP.
        Effect modification, interaction and mediation: an overview of theoretical insights for clinical investigators.
        Clin Epidemiol. 2017; 9: 331-338
        • Fleischer NL
        • Diez Roux AV
        Using directed acyclic graphs to guide analyses of neighbourhood health effects: An introduction.
        J Epidemiol Community Health. 2008; 62: 842-846
        • Graetz N
        • Boen CE
        • Esposito MH.
        Structural racism and quantitative causal inference: A life course mediation framework for decomposing racial health disparities.
        J Health Soc Behav. 2022; 63: 232-249
        • Westreich D
        • Greenland S.
        The table 2 fallacy: presenting and interpreting confounder and modifier coefficients.
        Am J Epidemiol. 2013; 177: 292-298