%inc 'C:\myfolders\glimmix.sas';
%macro glimmroc(y=,x_list=,z_list=,id=,c_s_d=, c_s_r=,weight=,dataset=);
%******* get working dataset DATAH ready *************;
%if %length(&dataset)>0 %then %do;
data datah; set &dataset; rename &id=id; /*rename &y=obs;*/
%end;
%else %if %length(&dataset)=0 %then %do;
data datah; set _last_; rename &id=id; /*rename &y=obs;*/
%end;
%******* get z_list *************;
%if %length(&z_list)>0 %then %do;
%let z=intercept%str( )&z_list;
%end;
%else %if %length(&z_list)=0 %then %do;
%let z=intercept;
%end;
%******* get c_s_d *************;
%if %length(&c_s_d)>0 %then %do;
%let csd=&c_s_d;
%end;
%else %if %length(&c_s_d)=0 %then %do;
%let csd=simple;
%end;
%******* get c_s_r *************;
%if %length(&c_s_r)>0 %then %do;
%let csr=&c_s_r;
%end;
%else %if %length(&c_s_r)=0 %then %do;
%let csr=cs;
%end;
%let wt = %qupcase(&weight);
%if %length(&weight)=0 %then %do;
%glimmix(data=datah,
stmts=%str(
class &id;
model &y = &x_list;
repeated/ type=&csr subject=&id;
random &z / type=&csd;
),
error=binomial, options=noprint
) ;
%end;
%else %do;
%glimmix(data=datah,
stmts=%str(
class &id ;
model &y = &x_list;
repeated/ type=&csr subject=&id;
random &z / type=&csd;
),
error=binomial, weight=&wt, options=noprint
) ;
%end;
%*******get the predicted probability****************;
data pred;
set _ds;
/*ptid=&id;*/
obs=&y;
run;
data data0;
set pred;
if obs=0;
run;
data data1;
set pred;
if obs=1;
Rsum1=0;
run;
data temp;
set pred;
ROC1=0;
run;
data out;
set temp;
keep ROC1;
run;
data _dataa_; set data0 (keep=id mu obs); run;
data _datab_; set data1 (keep=id mu obs Rsum1); run;
data _out_; set out; run;
/* start iml: interative matrix language evironment */
proc iml;
use _dataa_; read all into dataa;
use _datab_; read all into datab;
use _out_; read all into out;
start ROC(dataa,datab,out);
nra = nrow(dataa);
nrb = nrow(datab);
Rsum1=0;
if nrb < nra then do;
do i=1 to nrb;
temp = datab[i,2];
n1 = ncol(loc(dataa[,2] < temp));
n2 = ncol(loc(dataa[,2] = temp));
Rsum1 = Rsum1 + n1 + .5*n2;
end;
end;
else do;
do i=1 to nra;
temp = dataa[i,2];
n1 = ncol(loc(datab[,2] > temp));
n2 = ncol(loc(datab[,2] = temp));
Rsum1 = Rsum1 + n1 + .5*n2;
end;
end;
roc1 = Rsum1/(nra*nrb);
print roc1;
out[nrow(out),1] = roc1;
return(out);
finish ROC;
R=ROC(dataa,datab,out);
varnames={'ROC1'};
create outdata from R [colname=varnames] ;
append from R;
quit;
%*********get ROC curve*************;
data yy;
set pred;
/*if pred^=.;*/
do i=1 to 200;
if mu>0.005*i then y=1;
else if .z<mu<0.005*i then y=0;
else y=.;
output;
end;
run;
proc sort data=yy; by i; run;
%********get sensitivity and specificity*************;
proc freq data=yy;
tables y*obs/noprint out=pct outpct;
by i;
run;
data sen;
set pct;
if y=0 and obs=0;
sensi=PCT_COL;
cut=0.005*i;
run;
data spc;
set pct;
if y=1 and obs=1;
speci=PCT_COL;
cut=0.005*i;
run;
data curve1;
merge sen spc;
by cut;
_spc=100-speci;
run;
data curve;
set curve1;
sensi1=sensi/100;
_spc1=_spc/100;
run;
proc sort data=curve;
by _spc1;
run;
title "ROC curve";
symbol1 i=join v=star line=3 c=red;
axis1 order=(0 to 1 by 0.1);
axis2 order=(0 to 1 by 0.1) label=(a=90);
proc gplot data=curve;
plot sensi1*_spc1=1/vaxis=axis2 haxis=axis1;
label sensi1='Sensitivity';
label _spc1='1-Specificity';
run;
quit;
%mend glimmroc;
%glimmroc(y=var1, x_list=var2 , /*z_list= */,ID=id, c_s_d=vc, c_s_r=csh, dataset=mydataset);run;
Hi all,
I'm trying to create a ROC curve for repeated-measures data, following the procedure published in: Liu, H., & Wu, T. (2003). Estimating the area under a receiver operating characteristic (ROC) curve for repeated measures design. J Stat Softw, 8(12), 1-18.
My outcome is binary (0/1) and my predictor is a continuous trait, measured 1 to 9 times for each individual. I've been playing around with %GLIMMROC and I am able to get the ROC curve; however, I could not get the AUC (I obtained a ROC1=1, but an AUC of 1 does not corresponds to the area) neither the optimal cutoff for the predictor.
To note, I obtained only 178 specificity values (12 missing values)..do you think that this could affect the AUC calculation?
Any suggestion? I will include the SAS script I have been using.
Thank you!
This can be done directly using the GLIMMIX and LOGISTIC procedures as shown in this note. Note that another modeling approach for repeated binary data is to use a GEE model available in PROC GEE or GENMOD. An ROC analysis can be obtained in the same way for the GEE model.
Thanks for your reply, I will take a look to the suggested procedures.
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