If, at the subject level, the response is binary (such as yes/no, positive/negative) so that your data are aggregated binary data and you have the numerator and denominator counts making up the proportions, then you can fit a logistic model in procedures such as LOGISTIC, PROBIT, GENMOD, GAM, ADAPTIVEREG and others by using the events/trials syntax in the MODEL statement. The model assumes that the proportions represent a set of independent Bernoulli trials and have a binomial distribution. See the example in the Getting Started section of the PROC LOGISTIC documentation. But if your data are counts of some event over a period of time (such as person-years) such that the ratio of count/time is a rate that can exceed 1, then you would typically model the rate using a Poisson or negative binomial model with the log of the time variable as an offset. See the Poisson Regression example in the Getting Started section of the PROC GENMOD documentation.
