Abstract
Background
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.
Objective
Introduce the logit equation and provide a hands-on example to facilitate understanding
of its benefits and limitations.
Discussion
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.
Conclusions
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.
Keywords
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References
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Article info
Publication history
Accepted:
September 5,
2022
Received:
June 27,
2022
Footnotes
Reprints are not available from the authors.
Identification
Copyright
© 2022 Elsevier Inc. All rights reserved.
