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Example 5 for PROC LOGISTIC


/****************************************************************/
/*          S A S   S A M P L E   L I B R A R Y                 */
/*                                                              */
/*    NAME: LOGIEX5                                             */
/*   TITLE: Example 5 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:                                                     */
/*                                                              */
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/*****************************************************************
Example 5. Stratified Sampling
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/*
Consider the hypothetical example in Fleiss (1981, pp. 6-7) in which a test
is applied to a sample of 1000 people known to have a disease and to another
sample of 1000 people known not to have the same disease. In the diseased
sample, 950 were tested positively; in the nondiseased sample, only 10 were
tested positively. If the true disease rate in the population is 1 in 100,
you should specify PEVENT= .01 in order to obtain the correct false positive
and negative rates for the stratified sampling scheme. Omitting the PEVENT=
option is equivalent to using the overall sample disease rate (1000/2000 =
.5) as the value of the PEVENT= option and thereby ignoring the stratified
sampling.
*/

title 'Example 5. Stratified Sampling';

data Screen;
   do Disease='Present','Absent';
      do Test=1,0;
         input Count @@;
         output;
      end;
   end;
   datalines;
950  50
 10 990
;


proc logistic data=Screen;
   freq Count;
   model Disease(event='Present')=Test
         / pevent=.5 .01 ctable pprob=.5;
run;