provides maximum likelihood estimation for logistic regression, including the analysis of logits for dichotomous outcomes and the analysis of generalized logits for polychotomous outcomes. The CATMOD procedure can analyze data represented by a contingency table.

is a general modeling procedure for generalized linear models. It estimates parameters by maximum likelihood. Like the LOGISTIC procedure, it uses CLASS and MODEL statements in SAS/STAT procedures to form the statistical model and can fit models to binary and ordinal outcomes. The GENMOD procedure does not fit generalized logit models for nominal outcomes. However, the procedure also provides the capability of solving generalized estimating equations (GEE) to model correlated data and can perform a Bayesian analysis.

is a general modeling procedure for generalized linear mixed models. If the model does not contain random effects, the GLIMMIX procedure fits generalized linear models by the method of maximum likelihood. In the class of logistic regression models, the procedure can fit models to binary, binomial, ordinal, and nominal outcomes.

is specifically designed for logistic regression and estimates parameters by maximum likelihood. The procedure fits the usual logistic regression model for binary data as well as models with cumulative link function for ordinal data (such as the proportional odds model) and the generalized logit model for nominal data. The LOGISTIC procedure offers a number of variable selection methods and can perform conditional and exact conditional logistic regression analysis.

calculates maximum likelihood estimates of regression parameters and the natural (or threshold) response rate for quantal response data from biological assays or other discrete event data. This includes probit, logit, ordinal logistic, and extreme value (or gompit) regression models.

is designed for logistic regression and estimates parameters by maximum likelihood. The procedure incorporates complex survey sample designs, including designs with stratification, clustering, and unequal weighting.