[ LOGISTIC ] [ OPTIONS ] [ OUTPUT_GROUPS ] [ STATEMENTS ]
The LOGISTIC procedure fits logistic regression models to complex sample survey data and other clustered data applications. LOGISTIC produces estimates of the model parameters and their standard errors, and tests the null hypothesis that individual regression coefficients associated with each variable in the model are equal to zero. LOGISTIC also provides tests for overall model significance, model minus intercept, as well as model main effects and interactions. In addition, you can test linear combinations of the model parameters or output the parameter estimates and variance-covariance matrix to a data set for further hypothesis testing. You can also estimate and test linear combinations of the conditional and predicted marginals (generalizations of adjusted group means to non-linear models.)
The LOGISTIC procedure estimates model parameters using generalized estimating equations (GEE). A choice of independent vs. exchangeable "working" correlations is also provided. For estimating variance of the parameter estimates, LOGISTIC implements two robust methods described in Binder (1983) and Zeger and Liang (1986), as well as a model-based (naive) variance estimation method.
NOTE: For SAS-Callable SUDAAN, the name LOGISTIC conflicts with a SAS procedure of the same name. Use RLOGIST to invoke the SUDAAN logistic regression procedure.