LOGISTIC Procedure

The new SCORE statement enables you to score new data sets and compute fit statistics and ROC curves without refitting the model. Information for a fitted model can be saved to a SAS data set with the OUTMODEL= option, while the INMODEL= option inputs the model information required for the scoring.

The new STRATA statement enables you to perform conditional logistic regression on highly stratified data using the method of Gail, Lubin, and Rubenstein (1981). The OFFSET option is now enabled for logistic regression.

The LOGISTIC procedure now forms classification groups using the full formatted length of the CLASS variable levels.

Several new CLASS parameterizations are available: ordinal, orthogonal effect, orthogonal reference, and orthogonal ordinal.

You can now output the design matrix using the new OUTDESIGN= option.

The definition of concordance has been changed to make it more meaningful for ordinal models. The new definition is consistent with that used in previous releases for the binary response model.

9.1  

Enhancements for the exact computations include

• improved performance

• Monte Carlo method

• mid- p confidence intervals

For an exact conditional analysis, specifying the STRATA statement performs an efficient stratified analysis. The method of Mehta, Patel, and Senchaudhuri (1992), which is more efficient than the Hirji, Tsiatis, and Mehta (1989) algorithm for many problems, is now available with the METHOD=NETWORK option.