Logistic regression is primarily used for which type of outcome?

Logistic regression is a statistical method primarily utilized to analyze and predict outcomes that are categorical in nature, particularly situations where the dependent variable takes on two or more discrete categories. The most common application of logistic regression is in binary classification, where the outcome can be one of two possible values, such as success/failure or presence/absence of a condition.

In the context of maternal-fetal medicine, logistic regression can be invaluable for investigating the probability of an event occurring, such as the likelihood of a specific complication during pregnancy. The model evaluates how various predictor variables influence the odds of the outcome categories, allowing for effective risk assessment and decision-making.

While other types of regression exist for different outcome variables, logistic regression's focus on categorical outcomes differentiates it from those designed for continuous or ordinal outcomes, where different statistical techniques (like linear regression for continuous and ordinal regression for ordered categories) would be more appropriate. This specificity in functionality makes logistic regression a powerful tool for analyzing categorical dependent variables in medical research and beyond.