Logistic Regression in Clinical Studies

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Abstract

•A logistic regression model is used when the outcome of interest is binary. The term “logistic” refers to the underlying “logit” (log odds) function that is used to model the binary outcome. •Odds ratios are produced from a logistic regression model, and have a useful interpretation. •Tips, tricks and concepts used to fit logistic regression models are similar to those used in linear regression models. •Modeling building that is knowledge-based rather than automatic is preferred in most applications of logistic regression. •A logistic regression model that is overparameterized (ie too many variables for too few events) can result in odds ratios that are implausibly large and confidence intervals that are wide and uninterpretable. These types of “overfitted” models should be avoided. •Logistic regression models can be fit using most standard statistical software. •A logistic regression model is used when the outcome of interest is binary. The term “logistic” refers to the underlying “logit” (log odds) function that is used to model the binary outcome. •Odds ratios are produced from a logistic regression model, and have a useful interpretation. •Tips, tricks and concepts used to fit logistic regression models are similar to those used in linear regression models. •Modeling building that is knowledge-based rather than automatic is preferred in most applications of logistic regression. •A logistic regression model that is overparameterized (ie too many variables for too few events) can result in odds ratios that are implausibly large and confidence intervals that are wide and uninterpretable. These types of “overfitted” models should be avoided. •Logistic regression models can be fit using most standard statistical software.