<![CDATA[Background: Logistic regression is a widely used multivariable statistical method in health and allied research for examining associations and predicting categorical outcomes. However, its appropriate application requires an understanding of the type of outcome variable, model specification, variable coding, and underlying assumptions. This article provides an accessible introduction to logistic regression for non-statisticians, clinical professionals, and early-career researchers. Methods: This methodological article reviews the purpose, methods, mathematical concepts, variable requirements, sample size considerations, and assumptions of logistic regression. It distinguishes binary, multinomial, and ordinal logistic regression and explains odds, logit transformation, regression coefficients, odds ratios, predicted probabilities, reference category selection, and dummy variable coding. Results: Binary logistic regression is appropriate for dichotomous outcomes, multinomial logistic regression for nominal outcomes with more than two categories, and ordinal logistic regression for ordered categorical outcomes. Appropriate coding and selection of reference categories are essential for interpretation. Although a minimum of 10 outcome events per estimated model parameter has traditionally been used as a rule of thumb, sample size requirements depend on the characteristics and purpose of the model. Logistic regression does not require normally distributed predictors but requires attention to model assumptions and diagnostics. Conclusions: This article provides an accessible foundation for understanding and applying logistic regression in health and allied research and prepares readers for Part II, which focuses on binary logistic regression.]]>
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