How to integrate multiple imputation into Donna Spiegelman's SAS Subtype Macro?

Question

I am trying to analyse the HRs for different disease subtypes in a nested case-control study using the dataset with a high proportion of missing values for two variables that I need for adjusted risk model. For non-adjusted model I used Donna Spiegelman's Subtype macro https://www.hsph.harvard.edu/donna-spiegelman/software/subtype/.

Now I want to run an adjusted model using the dataset with Multiply imputed Data for categorical variables a and b. My idea was to first run Proc MI :

proc mi data=Subtype nimpute=100 out=ImputSP seed=1305468 ;
class a b disease_sub infection ; 
fcs plots=trace (  std mean );
var  a  b  age disease_sub infection;
fcs discrim( a b /classeffects=include)  nbiter=100;

run;

And then run the edited version of the subtype macro in which I've added "by imputation" option in 3 parts of the macro code.

/* get list of values of outcometype */
     proc sort data=newdatname;  by outcometype ;  run;
     proc means noprint data=newdatname;  var outcometype;
         output  out=_expblist_  mean=m_outcometype;
         by outcometype;
         where outcometype ne . ;
     run;

     data _expblist_;
        set _expblist_  end=_end_;
        call symput('_eb_'||trim(left(_n_)), trim(left(m_outcometype)));
        if _end_ then call symput ('_neb_', trim(left(_n_)));
      run;


%if &studydesign eq CACO %then %do; %let reftype=0; %end;

ods listing close;
proc logistic data=newdatname outest=LL;
by _imputation_;
    model outcometype(ref="&reftype")=&exposureND &exposureD_ &unconstrvar /link=glogit covb;

    %if &studydesign eq CACA %then %do;
        %do j=1 %to &_nexpND;

*variables for pair-wise LRT of heterogeneity test;
   
%do j=1 %to &_nexpND;
       %do i=1 %to &_neb_;
           if outcometype=&i then _expND_&j=&&_expND_&j._&i..;
       %end;
       rename _expND_&j=&&_expND_&j;
   %end;

   %do j=1 %to &_nexpD;
      %do k=1 %to &&_nexpD_&j;
          %do i=1 %to &_neb_;
             if outcometype=&i then _expD_&j._&k=&&_expD_&j._&k._&i..;
          %end;
      rename _expD_&j._&k=&&_expD_&j._&k..;

     %end;
   %end;

    %do j=1 %to &_nexpND;
    %do i=1 %to %eval(&_neb_-1);
        %do k=%eval(&i+1) %to &_neb_;
            _expND_&j._&i._vs_&j._&k.= _expND_&j*(outcometype=&&_eb_&i|outcometype=&&_eb_&k);
        %end;
    %end;
    %end;

    %do j=1 %to &_nexpD;
    %do i=1 %to %eval(&_neb_-1);
        %do k=%eval(&i+1) %to &_neb_;
             %do m=1 %to &&_nexpD_&j;
               _expD_&j._&m._&i._vs_&j._&m._&k.=_expD_&j._&m.*(outcometype=&&_eb_&i|outcometype=&&_eb_&k);
             %end;
        %end;
    %end;
    %end;
run;

%end;



ods listing close;

proc phreg data=newdatname outest=LL %if "&covs" eq "YES" %then %do; covs %end; nosummary;
        model (entrytime,time)*censoring(0)=

        %do j=1 %to &_nexpND;
          %do i=1 %to &_neb_;
             _expND_&j._&i
            %end;
        %end;
        %do j=1 %to &_nexpD;
             %do k=1 %to &&_nexpD_&j;
                  %do i=1 %to &_neb_;
                       _expD_&j._&k._&i
                   %end;
               %end;
         %end;

%if "&augmented" eq "NO" %then %do;
            %do j=1 %to &_nunconstrvar;
               %do i=1 %to &_neb_;
                _ucv_&j._&i
            %end;
         %end;
%end;

%else %if "&augmented" eq "YES" %then %do;
    &unconstrvar
%end;

      &constrvar / covb  %if "&studydesign" eq "MCACO" | "&studydesign" eq "CACO" %then %do; ties=discrete %end; ;
     %if "&studydesign" eq "COHORT" | "&studydesign" eq "CACO"  %then %do;  strata outcometype &stratavar; %end;
     %if "&studydesign" eq "MCACO" %then %do;   strata &matchid; %end;

    by _imputation_;

After running the subtypeBY_macro my intention is to run this code to obtain the final estimates:

proc print data=&PamEst (obs=8) ;
title "Logistic Model Coefficients First 8 Obs  ";
var _Imputation_ Variable Estimate StdErr;
run; 



TITLE "MULTIPLE IMPUTATION LOG REFRESSION-FCS";
proc mianalyze parms (classvar=ClassVal)=&PamEst  ; 
class &classmi  ;
MODELeffects &modelvar ; 
ods output ParameterEstimates=mianalyze_&PamEst;
run; 

title; 

data OR_&PamEst;
set mianalyze_&PamEst; 
OR=exp (estimate);
LCL_OR=exp (LCLMean); 
UCL_OR=exp(UCLMean);
proc print;
var parm &classmi OR LCL_OR UCL_OR;
run;

Sadly the program crushes on the SubtypeBY step and I do not know how to best proceed to integrate multiple imputation into the adjusted model. Any tips would be much appreciated!