Logistic regression is a statistical method used to analyze the relationship between one or more explanatory variables and a binomial response variable, such as the probability of an event occurring. It is similar to multiple linear regression but is used when the outcome is binary (e.g., death or survival). The method calculates the odds ratio, which represents the likelihood of an event occurring given the values of the explanatory variables. Logistic regression allows for the simultaneous analysis of multiple variables, reducing the risk of confounding effects. The article explains the concept of logistic regression using examples, highlighting its advantages over other methods like the Mantel-Haenszel odds ratio. It discusses the interpretation of logistic regression results, emphasizing the importance of understanding odds and probabilities, and how to handle continuous and categorical explanatory variables. The article also addresses the challenges of variable selection and the importance of choosing an appropriate reference level for categorical variables. Logistic regression is particularly useful in epidemiological studies, where multiple factors may influence an outcome. However, researchers must be cautious in model building to avoid spurious results and ensure that the model is appropriately validated. The article concludes that logistic regression is a powerful tool for analyzing complex relationships between variables, but its application requires careful consideration of the study design and data interpretation.Logistic regression is a statistical method used to analyze the relationship between one or more explanatory variables and a binomial response variable, such as the probability of an event occurring. It is similar to multiple linear regression but is used when the outcome is binary (e.g., death or survival). The method calculates the odds ratio, which represents the likelihood of an event occurring given the values of the explanatory variables. Logistic regression allows for the simultaneous analysis of multiple variables, reducing the risk of confounding effects. The article explains the concept of logistic regression using examples, highlighting its advantages over other methods like the Mantel-Haenszel odds ratio. It discusses the interpretation of logistic regression results, emphasizing the importance of understanding odds and probabilities, and how to handle continuous and categorical explanatory variables. The article also addresses the challenges of variable selection and the importance of choosing an appropriate reference level for categorical variables. Logistic regression is particularly useful in epidemiological studies, where multiple factors may influence an outcome. However, researchers must be cautious in model building to avoid spurious results and ensure that the model is appropriately validated. The article concludes that logistic regression is a powerful tool for analyzing complex relationships between variables, but its application requires careful consideration of the study design and data interpretation.