There is a SAS macro that may help.

LIBNAME dv0170 '\quan';
OPTIONS LS=80 PS=59 DATE NUMBER;

*program roc_corr.sas, from SAS web site for comparing 3 correlated ROC
curves;

*to tailor to a 2 var case: modify %vars and %contrast statements, Area
under curve=Mann-Whitney Statistics, MANWHIT;

proc printto new print = '\quan\x.out' log= '\quan\x.out';


/*
  * This is a SAS, largely SAS/IML, program to compute Mann-Whitney
  * statistics and component based estimates of their variance
  * covariance matrix. The method is closely related to the jackknife.
  * The method of components is well described in chapter 3 of Puri and
  * Sen, Multivariate Nonparametric Methods and Randle and Wolf's book
  * on nonparametrics. This program should run on either version
  * 5 or version 6 of SAS. To use this program you must set the
  * following macro variables:
  *
  * &indata set to the name of the data set.
  * &vars set to the names of the variables.
  * &ind set to a zero/one indicator variable
  * &contrast set to a contrast of interest. The
  * variables are assumed to be in the
  * same order as given in the &vars macro.
  *
  * This data set was used in the paper:
  * "Comparing the Areas Under Two or More Correlated Receiver
  * Operating Characteristic Curves: A Nonparametric Approach",
  * ER DeLong, DM DeLong and DL Clarke-Pearson, Biometrics 44,
  * p. 837-845.
  * The results vary somewhat from the paper because this program
  * compute components based on pairwise deletion of missing
  * observations, whereas the orginal analysis used pairwise deletion
  * after the components were calculated. The current strategy is
  * felt to be more conservative with respect to possible association
  * of response with the probability of the response not being observed.
  *************************************************************************/

%let indata = roc ; * name of data set;
%let vars = totscore alb tp; * variables ;
%let ind = popind; * population indicator variable;
                                    * contrast ;
%let contrast = %str( { 1 -1 0, 1 0 -1} );

data roc;
  input alb tp totscore popind;
  totscore = 10 - totscore;
  cards;
  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
   . . 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
   . . 6 1
  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
   . . 8 1
  2.8 6.2 8 0
   . . 7 1
   . . 7 1
  4.0 7.0 7 1
  3.3 5.7 6 1
  3.7 6.9 5 1
  2.0 . 7 1
  3.6 6.6 5 1
;

data comp; set &indata(keep = &vars &ind);
  if &ind ^= 0 and &ind ^= 1 then do;
    put 'indicator is not zero/one';
    delete;
  end;

proc iml;

start mwcomp(psi,z);
  *;
  * program to compute the man-whitney components ;
  * z is (nn by 2);
  * z[,1] is the column of data values;
  * z[,2] is the column of indicator variables;
  * z[i,2]=1 if the observation is from the x population;
  * z[i,2]=0 if the observation is from the y population;
  *
  * psi is the returned vector of u-statistic components;

  rz = ranktie( z[,1] ); * average ranks;
  nx = sum( z[,2] ); * num. of x's ;
  ny = nrow(z)-nx; * num of y's ;
  loc = loc( z[,2]=1 ); * x indexes ;
  psi = j(nrow(z),1,0);
  psi[loc] = (rz[loc] - ranktie(z[loc,1]))/ny; * x components ;
  loc = loc( z[,2]=0 ); * y indexes ;
  psi[loc] = ( nx+ranktie(z[loc,1])-rz[loc])/nx; * y components ;
  free rz loc nx ny; * free space ;
  finish;

start mwvar(t,v,nx,ny,z);
  *;
  * compute man-whitney statistics and variance;
  * input z, n by (k+1);
  * z[,1:k] are the different variables;
  * z[,k+1] are indicator values,
  * 1 if the observation is from population x and ;
  * 0 if the observation is from population y;
  * t is the k by k vector of estimated statistics;
  * the (i,j) entry is the MannWhitney statistic for the
  * i-th column when used with the j-th column. The only
  * observations with nonmissing values in each column are
  * used. The diagonal elements are, hence, based only on the
  * single column of values.
  * v is the k by k estimated variance matrix;
  * nx is the matrix of x population counts on a pairwise basis;
  * ny is the matrix of y population counts on a pairwise basis;

  k = ncol(z)-1;
  ind = z[,k+1];
  v = j(k,k,0); t=v; nx=v; ny=v;

  * The following computes components after pairwise deletion of
  * observations with missing values. If either there are no missing
  * values or it is desired to use the components without doing
  * pairwise deletion first, the nested do loops could be evaded.
  *;
  do i=1 to k;
    do j=1 to i;
       who = loc( (z[,i]^=.)#(z[,j]^=.) ); * nonmissing pairs;
       run mwcomp(psii,(z[,i]||ind)[who,]); * components;
       run mwcomp(psij,(z[,j]||ind)[who,]);
       inow = ind[who,]; * x's and y's;
       m = inow[+]; * current x's;
       n = nrow(psii)-m; * current y's;
       nx[i,j] = m; ny[i,j] = n;
       mi = (psii#inow)[+] / m; * means;
       mj = (psij#inow)[+] / m;
       t[i,j] = mi; t[j,i] = mj;
       psii = psii-mi; psij = psij-mj; * center;
       v[i,j] = (psii#psij#inow)[+] / (m#(m-1))
              + (psii#psij#(1-inow))[+] / (n#(n-1));
       v[j,i] = v[i,j];
    end;
  end;
  free psii psij inow ind who;
  finish;

  /* start of execution of the IML program */

  use comp var {&vars &ind};
  read all into data [colname=names];

  run mwvar(t,v,nx,ny,data); * estimates and variances;

  vname = names[1:(ncol(names)-1)];
  manwhit = vecdiag(t);
  print 'Pairwise Deletion Mann-Whitney Statistics',
         t [colname=vname rowname=vname];

  print 'Mann-Whitney Statistics', manwhit[ rowname=vname];

  print 'Estimated Variance Matrix', v [colname=vname rowname=vname];

  c=sqrt( vecdiag(v) ); c=v / (c@c`);
  print 'Estimated Correlations', c [colname=vname rowname=vname];

  print 'X populations sample sizes', nx [colname=vname rowname=vname];
  print 'Y populations sample sizes', ny [colname=vname rowname=vname];

  l = &contrast ;
  print 'Contrast of Interest', l [colname=vname];

  lt=l*manwhit;
  print 'Estimates of Contrast', lt;
  lv=l*v*l`;
  print 'Variance Estimates of Contrast', lv;

  c = ginv(lv);

  chisq = lt`*c*lt;
  df = trace(c*lv);
  p = 1 - probchi( chisq, df );
  chiname = {'Chisq' 'DF' 'Prob>Chi'};
  print (chisq||df||p) [colname=chiname];

  quit;






Date: Thu, 7 Feb 2002 09:53:35 -0400
From: Paul H Artes <[log in to unmask]>
Subject: ROC and confidence intervals
Dear Allstaters,
1.) can anyone point me to some software that implements ER DeLongs methods
for comparing the area under several correlated ROC curves (ie from different
clinical tests on the same sample of subjects).
2.) Sometimes it may be more clinically descriptive to state the sensitivity
at a given, fixed specificity, eg. 95%. I am unsure on how to derive the CI
for
that sensitivity: can I just use the CI of the proportion (from binomial
dist), or
do I have to take into account that the specificity (fixed at 90%) is in
itself
a random variable which will fluctuate from sample to sample.
I'd be most grateful for any advice.
Paul
*******************************************************************
Paul H Artes | e-mail: [log in to unmask]
Department of Ophthalmology | office tel: (902) 473-3240
Dalhousie University | fax: (902) 473-2839
Halifax, Nova Scotia, CANADA |
*******************************************************************