Logistic Regression in Practice

This article deals with the use of logistic regression to model data, where the response variable is binary and there are potentially numerous explanatory or predictor variables. Methods of estimating the coefficients in the model and procedures for hypothesis testing are considered. The correspondence of the coefficients of the logistic regression model to the odds ratio is noted along with methods for computing point estimates and confidence intervals for the odds ratio. Interpretation of coefficients for dichotomous, polytomous, and continuous independent variables is carefully developed. Confounding and effect modification are easily dealt with through the use of this modeling technique. All these concepts are illustrated through the analysis of low birth weight data. Keywords: binary response; logit transformation; maximum likelihood; likelihood ratio test; odds ratio; confounding; effect modification