Binary logistic regression models are based on a dependent variable that can take on only one of two values, such as presence or absence of a disease, deceased or not deceased, married or unmarried, and so on. In this setting, the independent (sometimes called explanatory or predictor) variables are used for predicting the probability of occurrence of an outcome (such as mortality).

The logistic regression model is sometimes called a logit model. Logistic analysis methods are available for cases in which the dependent variable takes on more than two values, but this topic is beyond the scope of this book and we will discuss only the binary logistic model. Logistic analysis is used to create an equation that can be used to predict the probability of occurrence of the outcome of interest, to assess the relative importance of independent variables, and to calculate odds ratios (OR) that measure the importance of an independent variable relative to the response. The independent variables can be either continuous or categorical.

16.1 LOGISTIC ANALYSIS BASICS

Before describing the SAS implementation of logistic regression, we briefly discuss some ...

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