Hi,
Maybe I found a workaround way to do this.
Suppose you have data set:
data Data1;
call streaminit(123);
do ID=1 to 63;
do Outcome = 1 to 0 by -1;
input Gall Hyper @@;
x1=rand('normal');
x2=rand('lognormal');
x3=rand('exponent',0.2);
x4=rand('gamma',1,2);
x5=rand('integer',1,200);
x6=rand('normal',1,2);
x7=rand('normal',2,3);
x8=rand('normal',3,4);
x9=rand('normal',4,5);
x10=rand('normal',5,6);
output;
end;
end;
datalines;
0 0 0 0 0 0 0 0 0 1 0 1 0 0 1 0 1 0 0 1
0 1 0 0 1 0 0 0 1 1 0 1 0 0 0 0 0 0 0 0
1 0 0 0 0 0 0 1 1 0 0 1 1 0 1 0 1 0 0 1
0 1 0 0 0 0 1 1 0 0 1 1 0 0 0 1 0 1 0 0
0 0 1 1 0 1 0 1 0 1 0 0 0 0 0 0 0 0 0 0
0 0 0 1 1 0 0 1 0 0 0 1 1 0 0 0 0 1 0 0
0 1 0 0 0 1 0 0 0 1 0 0 0 0 0 0 1 1 1 1
0 0 0 1 0 1 0 0 0 1 0 1 0 1 0 1 0 1 0 0
0 0 0 0 0 1 1 0 0 0 0 1 0 0 0 0 1 0 0 0
0 0 0 0 1 1 0 0 0 1 0 0 0 0 0 0 0 1 0 1
0 0 0 0 0 1 0 1 0 1 0 0 0 1 0 0 1 0 0 0
0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 1 1 0 0
1 0 1 0 0 1 0 0 1 0 0 0
;
1) Get the design matrix with RANDOM effect by PROC GLIMMIX:
https://blogs.sas.com/content/iml/2016/02/24/create-a-design-matrix-in-sas.html
proc glimmix data=Data1 outdesign(names )=MixedDesign nofit;
class Gall Hyper;
model outcome(event='1')=Gall Hyper x1-x10/dist=binary;
random ID;
ods select ColumnNames;
run;

2) Perform LASSO method via PROC HPGENSELECT:
Any variable name start with "_X" and "_Z" is from Design Matrix .
Any variable name start with "_X" is fixed effect.
Any variable name start with "_Z" is random effect.
proc hpgenselect data=MixedDesign;
model outcome(event='1') = _x: _z:/ dist=binary;
selection method=Lasso(choose=SBC) details=all;
performance details;
run;

_X10 _X14 _X11 _X12 _X2 _X6 _X9 _X4 entered model.
NOTE: if any one ofvariables(_X2 _X3 _X4 _X5 are from category variable Gall and Hyper ) entered model ,you need include these category variables in model.
_X10 _X14 _X11 _X12 _X2 _X6 _X9 _X4
correspond to variables:
Gall Hyper x5 x9 x6 x7 x1 x4
So include these variables in next proc logistic.
3)Perform Condition Logistic Regression via PROC LOGISTIC.
proc logistic data=Data1;
strata ID;
class Gall Hyper;
model outcome(event='1')=Gall Hyper x5 x9 x6 x7 x1 x4 ;
run;