Performing Exact Logistic Regression with the SAS

Exact logistic regression has become an important analytical technique, especially in the pharmaceutical industry, since the usual asymptotic methods for analyzing small, skewed, or sparse data sets are unreliable. Inference based on enumerating the exact distributions of sufficient statistics for parameters of interest in a logistic regression model, conditional on the remaining parameters, is computationally infeasible for many problems. Hirji, Mehta, and Patel (1987) developed an efficient algorithm for generating the required conditional distributions, thus making these methods computationally available. This paper discusses the theory and methods for exact logistic regression and illustrates their application in Version 8 of the SAS R System with new facilities in the LOGISTIC procedure.