## Example 8 for PROC LOGISTIC

```
/****************************************************************/
/*          S A S   S A M P L E   L I B R A R Y                 */
/*                                                              */
/*    NAME: LOGIEX8                                             */
/*   TITLE: Example 8 for PROC LOGISTIC                         */
/* PRODUCT: STAT                                                */
/*  SYSTEM: ALL                                                 */
/*    KEYS: logistic regression analysis,                       */
/*          binomial response data,                             */
/*   PROCS: LOGISTIC                                            */
/*    DATA:                                                     */
/*                                                              */
/* SUPPORT: Bob Derr                                            */
/*     REF: SAS/STAT User's Guide, PROC LOGISTIC chapter        */
/*    MISC:                                                     */
/*                                                              */
/****************************************************************/

/*****************************************************************
Example 8. Comparing Receiver Operating Characteristic Curves
*****************************************************************/

/*
Delong, Delong, and Clarke-Pearson (1988) report on 49 patients with ovarian
cancer who also suffer from an intestinal obstruction.  Three (correlated)
screening tests are measured to determine whether a patient will benefit from
surgery.  The three tests are the K-G score and two measures of nutritional
status: total protein and albumin.

This example illustrates how to use PROC LOGISTIC to compute and display the
ROC curves for the three tests, and how to compare the tests.
*/

title 'Example 8: Comparing ROC Curves';

data roc;
input alb tp totscore popind @@;
totscore = 10 - totscore;
datalines;
3.0 5.8 10 0   3.2 6.3  5 1   3.9 6.8  3 1   2.8 4.8  6 0
3.2 5.8  3 1   0.9 4.0  5 0   2.5 5.7  8 0   1.6 5.6  5 1
3.8 5.7  5 1   3.7 6.7  6 1   3.2 5.4  4 1   3.8 6.6  6 1
4.1 6.6  5 1   3.6 5.7  5 1   4.3 7.0  4 1   3.6 6.7  4 0
2.3 4.4  6 1   4.2 7.6  4 0   4.0 6.6  6 0   3.5 5.8  6 1
3.8 6.8  7 1   3.0 4.7  8 0   4.5 7.4  5 1   3.7 7.4  5 1
3.1 6.6  6 1   4.1 8.2  6 1   4.3 7.0  5 1   4.3 6.5  4 1
3.2 5.1  5 1   2.6 4.7  6 1   3.3 6.8  6 0   1.7 4.0  7 0
3.7 6.1  5 1   3.3 6.3  7 1   4.2 7.7  6 1   3.5 6.2  5 1
2.9 5.7  9 0   2.1 4.8  7 1   2.8 6.2  8 0   4.0 7.0  7 1
3.3 5.7  6 1   3.7 6.9  5 1   3.6 6.6  5 1
;

ods graphics on;
proc logistic data=roc plots=roc(id=prob);
model popind(event='0') = alb tp totscore / nofit;
roc 'Albumin' alb;
roc 'K-G Score' totscore;
roc 'Total Protein' tp;
roccontrast reference('K-G Score') / estimate e;
run;
ods graphics off;

```