Thank you KurtBremser. The log does come from the code as posted. I upload some pictures, which shows more clearly. Thank you again! ... View more

Thank you ChrisNZ. The log did come out from the code. I upload some pictures, which shows more clearly. Thank you again! ... View more

Thank you Kawakami. There are no other code example in the paper. Though the log is long, there are mainly two errors. The one is ERROR 180-322, and the other is ERROR 22-322 and ERROR 200-322. Are these errors fixable? Thank you again! ... View more

Hello all, I was hoping to find some help regarding this macro, which is from a paper named "A SAS macro for estimation of direct adjusted survival curves based on a stratified Cox regression model". However, when I try to apply the macro using the following code:   libname data "C:\Research 2\datacheck\datacheck1"; %INCLUDE 'ADJSURV.sas'; %_ADJSURV (data=data.cox , time=time , event=event , group=group , x=sex1 age1 comorbidities1 comorbidities2 comorbidities3 comorbidities4 comorbidities5 comorbidities6 concurrent1 concurrent2 concurrent previous after , model=1 , out=data.coxout);   I received a lot of error:   I am having trouble identifying the cause, and I am sure this would be a simple resolution for someone with more experience utilizing macros. Any help would be very much appreciated. Thank you!   Here is the code from the paper:  /***************************************************************** _ADJSURV calculates the direct adjusted survival probabilities for K treatment groups at predetermined time points, based on a regular Cox model (Model=2) or a stratified Cox regression model (Model=1). Macro parameters: inputdata - the input sas data name; time - the survival time variable; event - the event indicator; group - the treatment group variable, which must take values 1,...,K for K<10 groups covlist - a list of covariate names; model - 1 if a stratified Cox model is selected, 2 if a regular Cox model is selected; outdata - the output sas data name. The output dataset contains: time - the event times survi, i=1,...,K sei, i=1,...,K seij, 1<=i<j<=K Authored by Xu Zhang 2007 Re-formatted by Rodney Sparapani 02/17/2014 *****************************************************************/ %*macro ADJSURV(inputdata, time, event, group, covlist, model, outdata); %macro _ADJSURV(data=REQUIRED, out=REQUIRED, time=REQUIRED, event=REQUIRED, group=REQUIRED, x=REQUIRED, model=1, inputdata=&data, outdata=&out, covlist=&x); %_require(&data &time &event &group &out &x) %local numgroup numcov i j adj1 adj2 group iter strata1 strata2; %let covlist=%_list(&covlist); %* 08/18/15; proc means data=&inputdata noprint; var &group; output out=maxout max(&group)=numgroup; run; data _null_; set maxout; call symput('numgroup', numgroup); run; proc iml; use &inputdata; read all var {&covlist} into x; close &inputdata; numcov=ncol(x); create ncovout from numcov[colname='numcov']; append from numcov; close ncovout; run; quit; data _null_; set ncovout; call symput('numcov', numcov); run; %if &model=2 %then %do; /************************************/ /* */ /* Model 2 : A regular Cox model */ /* */ /************************************/ /*************************************************/ /* */ /* Assign names to variables in the input data. */ /* */ /*************************************************/ proc iml; use &inputdata; read all var {&time} into time; read all var {&event} into event; read all var {&group} into group; read all var {&covlist} into x; close &inputdata; numobs=nrow(time); gmat=j(&numgroup, &numgroup-1, 0); do i=2 to &numgroup; gmat[i, i-1]=1; end; zmat=j(numobs, &numgroup-1+&numcov, 0); do i=1 to numobs; zmat[i,]=gmat[group[i],]||x[i,]; end; out=time||event||zmat; names={'time' 'event' %do i=1 %to &numgroup-1+&numcov; "z&i" %end;}; create indata from out[colname=names]; append from out; close indata; quit; /**********************************************/ /* */ /* Get regression coefficient estimates from */ /* proc phreg, read in these estimates and */ /* calculate s0(b,t), s1(b,t). */ /* */ /**********************************************/ ODS LISTING CLOSE; proc freq data=indata; where event=1; table time/out=dcount; run; ODS LISTING; proc sort data=indata; by descending time event; run; proc phreg data=indata covout outest=best noprint; model time*event(0)= %do i=1 %to &numgroup-1+&numcov; z&i %end;; output out=coxout xbeta=zb; run; proc sort data=coxout; by descending time; run; data best sigma; set best; if _type_='PARMS' then output best; if _type_='COV' then output sigma; run; data riskset; set coxout; by descending time; keep time s0 s1:; s0+exp(zb); %do i=1 %to &numcov+&numgroup-1; s1_&i+z&i * exp(zb); %end; if event; run; data riskset; set riskset; by descending time; if last.time; run; proc sort data=riskset; by time; run; data riskset; merge riskset dcount (keep=time count); by time; run; /****************************************/ /* */ /* Get weighted survival function */ /* estimate and its variance estimate */ /* */ /****************************************/ proc iml; use riskset; read all var{time} into time; read all var{s0} into s0; read all var{%do i=1 %to &numgroup-1+&numcov; s1_&i %end;} into s1; read all var{count} into count; close riskset; use best; read all var{%do i=1 %to &numgroup-1+&numcov; z&i %end;} into b; close best; use sigma; read all var{%do i=1 %to &numgroup-1+&numcov; z&i %end;} into sigma; close sigma; use indata; read all var{%do i=&numgroup %to &numgroup-1+&numcov; z&i %end;} into zmat; close indata; numtime=nrow(time); numobs=nrow(zmat); numcov=ncol(s1); ctemp=0; wtemp=0; cumuhaz=j(numtime,1,0); w1=j(numtime,1,0); do i=1 to numtime; ctemp=ctemp+count[i]/s0[i]; wtemp=wtemp+count[i]/s0[i]/s0[i]; cumuhaz[i]=ctemp; w1[i]=wtemp; end; /********************************************************/ /* */ /* Do loop calculate the direct adjusted probabilities */ /* and their variance estimates at time 'tau'. */ /* */ /********************************************************/ g=j(&numgroup, &numgroup-1,0); do i=2 to &numgroup; g[i,i-1]=1; end; survmat=time; %do iter=1 %to &numgroup; zz=j(numobs, &numgroup-1+&numcov, 0); do i=1 to numobs; zz[i,]=g[&iter,]||zmat[i,]; end; adjsurv=j(numtime,1,0); fexpbz=j(numtime,1,0); fh=j(numtime,numcov,0); do i=1 to numobs; expbz=exp(zz[i,]*t(b)); surv=exp(-cumuhaz)##expbz; adjsurv=adjsurv+surv; fexpbz=fexpbz+surv#expbz; h=j(numtime,numcov,0); htemp=j(1,numcov,0); do j=1 to numtime; htemp=htemp+count[j]/s0[j]*(zz[i,]-s1[j,]/s0[j]); h[j,]=htemp; end; fh=fh+surv#h#expbz; end; term1=(fexpbz##2)#w1; term2=j(numtime,1,0); do i=1 to numtime; term2[i]=fh[i,]*sigma*t(fh[i,]); end; varsurv=term1+term2; adjsurv=adjsurv/numobs; varsurv=varsurv/numobs/numobs; sesurv=varsurv##0.5; survmat=survmat||adjsurv||sesurv; %end; /*********************************************************/ /* */ /* Calculate covariance estimates between two direct */ /* adjusted survival probabilities. */ /* */ /*********************************************************/ %do adj1=1 %to &numgroup; z1=j(numobs, &numgroup-1+&numcov, 0); do i=1 to numobs; z1[i,]=g[&adj1,]||zmat[i,]; end; %do adj2=&adj1+1 %to &numgroup; z2=j(numobs, &numgroup-1+&numcov, 0); do i=1 to numobs; z2[i,]=g[&adj2,]||zmat[i,]; end; fe2_fe1=j(numtime,1,0); fh2_fh1=j(numtime,numcov,0); do i=1 to numobs; h1=j(numtime,numcov,0); htemp1=j(1,numcov,0); do j=1 to numtime; htemp1=htemp1+count[j]/s0[j]*(z1[i,]-s1[j,]/s0[j]); h1[j,]=htemp1; end; h2=j(numtime,numcov,0); htemp2=j(1,numcov,0); do j=1 to numtime; htemp2=htemp2+count[j]/s0[j]*(z2[i,]-s1[j,]/s0[j]); h2[j,]=htemp2; end; expbz1=exp(z1[i,]*t(b)); expbz2=exp(z2[i,]*t(b)); surv1=exp(-cumuhaz)##expbz1; surv2=exp(-cumuhaz)##expbz2; fe2_fe1=fe2_fe1+surv2#expbz2-surv1#expbz1; fh2_fh1=fh2_fh1+surv2#h2#expbz2-surv1#h1#expbz1; end; term1=(fe2_fe1##2)#w1; term2=j(numtime,1,0); do i=1 to numtime; term2[i]=fh2_fh1[i,]*sigma*t(fh2_fh1[i,]); end; covar=term1+term2; covar=covar/numobs/numobs; sqcov=covar##0.5; survmat=survmat||sqcov; %end; %end; names={'time' %do i=1 %to &numgroup; "surv&i" "se&i" %end; %do i=1 %to &numgroup; %do j=&i+1 %to &numgroup; "se&i.&j" %end; %end; }; create mout from survmat[colname=names]; append from survmat; close mout; run; quit; %end; %else %do; /***************************************/ /* */ /* Model 1 : a stratified Cox model */ /* */ /***************************************/ proc iml; use &inputdata; read all var {&time} into time; read all var {&event} into event; read all var {&group} into group; read all var {&covlist} into x; close &inputdata; out=time||event||group||x; names={'time' 'event' 'strata' %do i=1 %to &numcov; "z&i" %end;}; create indata from out[colname=names]; append from out; close indata; run; quit; /**********************************************/ /* */ /* Get regression coefficient estimates from */ /* proc phreg, read in these estimates and */ /* calculate s0(b,t), s1(b,t). */ /* */ /**********************************************/ proc sort data=indata; by descending time descending event; run; data alltime; set indata (keep=time event); by descending time; drop event; if first.time; if event; run; proc sort data=alltime; by time; run; proc phreg data=indata covout outest=best noprint; model time*event(0)=%do i=1 %to &numcov; z&i %end;; strata strata; output out=coxout xbeta=zb; run; proc sort data=coxout; by strata descending time; run; data best sigma; set best; if _type_='PARMS' then output best; else if _type_='COV' then output sigma; run; %do group=1 %to &numgroup; data riskset&group; set coxout (keep=time strata %do i=1 %to &numcov; z&i %end; zb); where strata=&group; by descending time; keep time s0 %do i=1 %to &numcov; s1_&i %end;; s0+exp(zb); %do i=1 %to &numcov; s1_&i+z&i * exp(zb); %end; if last.time; run; proc sort data=riskset&group; by time; run; ODS LISTING CLOSE; proc freq data=indata; where strata=&group & event=1; table time/out=dcount&group; run; ODS LISTING; data riskset&group; merge alltime (in=inall) riskset&group dcount&group (keep=time count); by time; drop ts:; retain ts0 %do i=1 %to &numcov; ts1_&i %end; 999; if inall; if count=. then count=0; if s0^=. then ts0=s0; else s0=ts0; %do i=1 %to &numcov; if s1_&i^=. then ts1_&i=s1_&i; else s1_&i=ts1_&i; %end; run; /*****************************************************/ /* */ /* Calculate direct adjusted survival probabilites */ /* and their variance estimates at time 'tau'. */ /* */ /*****************************************************/ proc iml; use riskset&group; read all var{time} into time; read all var{s0} into s0; read all var{%do i=1 %to &numcov; s1_&i %end;} into s1; read all var{count} into count; close riskset&group; use best; read all var{%do i=1 %to &numcov; z&i %end;} into b; close best; use sigma; read all var{%do i=1 %to &numcov; z&i %end;} into sigma; close sigma; use indata; read all var{%do i=1 %to &numcov; z&i %end;} into zmat; close indata; numtime=nrow(s0); numcov=ncol(s1); cumuhaz=j(numtime,1,0); w1=j(numtime,1,0); ctemp=0; wtemp=0; do i=1 to numtime; ctemp=ctemp+count[i]/s0[i]; wtemp=wtemp+count[i]/s0[i]/s0[i]; cumuhaz[i]=ctemp; w1[i]=wtemp; end; numobs=nrow(zmat); adjsurv=j(numtime,1,0); varsurv=j(numtime,1,0); fexpbz=j(numtime,1,0); fh=j(numtime,numcov,0); do i=1 to numobs; expbz=exp(zmat[i,]*t(b)); surv=exp(-cumuhaz)##expbz; adjsurv=adjsurv+surv; fexpbz=fexpbz+surv#expbz; h=j(numtime,numcov,0); htemp=j(1, numcov, 0); do j=1 to numtime; htemp=htemp + count[j]/s0[j]*(zmat[i,]-s1[j,]/s0[j]); h[j,]=htemp; end; fh=fh+surv#h#expbz; end; adjsurv=adjsurv/numobs; term1=(fexpbz##2)#w1; term2=j(numtime,1,0); do i=1 to numtime; term2[i]=fh[i,]*sigma*t(fh[i,]); end; varsurv=term1+term2; varsurv=varsurv/numobs/numobs; sesurv=varsurv##0.5; outmat=time||adjsurv||sesurv; names={'time' 'surv' 'se'}; create surv&group from outmat[colname=names]; append from outmat; close surv&group; run; quit; %end; /*******************************************************/ /* */ /* Calculate covariance estimates between two direct */ /* adjusted survival probabilities. */ /* */ /*******************************************************/ %do strata1=1 %to &numgroup; %do strata2=&strata1+1 %to &numgroup; proc iml; use riskset&strata1; read all var{time} into time; read all var{s0} into s01; read all var{%do i=1 %to &numcov; s1_&i %end;} into s11; read all var{count} into count1; close riskset&strata1; use riskset&strata2; read all var{s0} into s02; read all var{%do i=1 %to &numcov; s1_&i %end;} into s12; read all var{count} into count2; close riskset&strata2; use best; read all var{%do i=1 %to &numcov; z&i %end;} into b; close best; use sigma; read all var{%do i=1 %to &numcov; z&i %end;} into sigma; close sigma; use indata; read all var{%do i=1 %to &numcov; z&i %end;} into zmat; close indata; numtime=nrow(time); numobs=nrow(zmat); numcov=ncol(s11); cumuhaz1=j(numtime,1,0); cumuhaz2=j(numtime,1,0); w1=j(numtime,1,0); w2=j(numtime,1,0); ctemp1=0; ctemp2=0; wtemp1=0; wtemp2=0; do i=1 to numtime; ctemp1=ctemp1+count1[i]/s01[i]; ctemp2=ctemp2+count2[i]/s02[i]; wtemp1=wtemp1+count1[i]/s01[i]/s01[i]; wtemp2=wtemp2+count2[i]/s02[i]/s02[i]; cumuhaz1[i]=ctemp1; cumuhaz2[i]=ctemp2; w1[i]=wtemp1; w2[i]=wtemp2; end; fexpbz1=j(numtime,1,0); fexpbz2=j(numtime,1,0); fh2_fh1=j(numtime,numcov,0); do i=1 to numobs; expbz=exp(zmat[i,]*t(b)); surv1=exp(-cumuhaz1)##expbz; surv2=exp(-cumuhaz2)##expbz; fexpbz1=fexpbz1+surv1#expbz; fexpbz2=fexpbz2+surv2#expbz; h1=j(numtime,numcov,0); h2=j(numtime,numcov,0); htemp1=j(1, numcov, 0); htemp2=j(1, numcov, 0); do j=1 to numtime; htemp1=htemp1 + count1[j]/s01[j]*(zmat[i,]-s11[j,]/s01[j]); htemp2=htemp2 + count2[j]/s02[j]*(zmat[i,]-s12[j,]/s02[j]); h1[j,]=htemp1; h2[j,]=htemp2; end; fh2_fh1=fh2_fh1+surv2#h2#expbz-surv1#h1#expbz; end; term1=(fexpbz1##2)#w1; term2=(fexpbz2##2)#w2; term3=j(numtime,1,0); do i=1 to numtime; term3[i]=fh2_fh1[i,]*sigma*t(fh2_fh1[i,]); end; covar=term1+term2+term3; covar=covar/numobs/numobs; cov=covar##0.5; names={'se'}; create cov&strata1&strata2 from cov[colname=names]; append from cov; close cov&strata1&strata2; run; quit; %end; %end; data mout; merge %do group=1 %to &numgroup; surv&group (rename=(surv=surv&group se=se&group)) %end; %do i=1 %to &numgroup; %do j=&i+1 %to &numgroup; cov&i&j (rename=(se=se&i.&j)) %end; %end;; run; %end; /**************************************/ /* */ /* Make final output data */ /* */ /**************************************/ data &outdata; time=0; %do i=1 %to &numgroup; surv&i=1; se&i=0; %do j=&i+1 %to &numgroup; se&i.&j=0; %end; %end; output; run; data &outdata; set &outdata mout; by time; run; %mend;  and here is the log of SAS:   1 libname data "C:\Research 2\datacheck\datacheck1"; NOTE: Libref DATA was successfully assigned as follows: Engine: V9 Physical Name: C:\Research 2\datacheck\datacheck1 2 %INCLUDE 'ADJSURV.sas'; 708 %_ADJSURV (data=data.cox 709 , time=time 710 , event=event 711 , group=group 712 , x=sex1 age1 comorbidities1 comorbidities2 comorbidities3 comorbidities4 comorbidities5 712 ! comorbidities6 concurrent1 concurrent2 concurrent previous after 713 , model=1 714 , out=data.coxout); NOTE: Line generated by the invoked macro "_ADJSURV". 1 %require(&data &time &event &group &out &x)     -   180 WARNING: Apparent invocation of macro REQUIRE not resolved. WARNING: Apparent invocation of macro _LIST not resolved. ERROR 180-322: Statement is not valid or it is used out of proper order. NOTE: Line generated by the invoked macro "_ADJSURV". 3 proc means data=&inputdata noprint; var &group; output out=maxout                                                                 ---                                                                180 3 ! max(&group)=numgroup; run; data _null_; set maxout; call symput('numgroup', 3 ! numgroup); run; proc iml; use &inputdata; read all var {&covlist} into x; 3 ! close ERROR 180-322: Statement is not valid or it is used out of proper order. NOTE: Line generated by the invoked macro "_ADJSURV". 3 proc means data=&inputdata noprint; var &group; output out=maxout                                                                                     ------                                                                                    180 3 ! max(&group)=numgroup; run; data _null_; set maxout; call symput('numgroup', 3 ! numgroup); run; proc iml; use &inputdata; read all var {&covlist} into x; 3 ! close ERROR 180-322: Statement is not valid or it is used out of proper order. ERROR: File WORK.MAXOUT.DATA does not exist. NOTE: Numeric values have been converted to character values at the places given by: (Line):(Column). 1079:162 NOTE: The SAS System stopped processing this step because of errors. NOTE: DATA statement used (Total process time): real time 0.01 seconds cpu time 0.01 seconds NOTE: Writing HTML Body file: sashtml.htm NOTE: IML Ready NOTE: Data file DATA.COX.DATA is in a format that is native to another host, or the file encoding does not match the session encoding. Cross Environment Data Access will be used, which might require additional CPU resources and might reduce performance. NOTE: Line generated by the macro variable "COVLIST". 1 %_list(sex1 age1 comorbidities1 comorbidities2 comorbidities3 comorbidities4 comorbidities5     -    22    200 1 ! comorbidities6 concurrent1 concurrent2 concurrent previous after) WARNING: Apparent invocation of macro _LIST not resolved. ERROR 22-322: Syntax error, expecting one of the following: a name, a quoted string, a numeric constant, a datetime constant, a missing value, (, (|, ), *, ',', -, =, [, |, }. ERROR 200-322: The symbol is not recognized and will be ignored. NOTE: The data set WORK.NCOVOUT has 1 observations and 1 variables. NOTE: Module MAIN is undefined in IML; cannot be RUN. NOTE: Exiting IML. NOTE: PROCEDURE IML used (Total process time): real time 0.53 seconds cpu time 0.35 seconds NOTE: Numeric values have been converted to character values at the places given by: (Line):(Column). 4:197 NOTE: There were 1 observations read from the data set WORK.NCOVOUT. NOTE: DATA statement used (Total process time): real time 0.01 seconds cpu time 0.01 seconds NOTE: IML Ready NOTE: Data file DATA.COX.DATA is in a format that is native to another host, or the file encoding does not match the session encoding. Cross Environment Data Access will be used, which might require additional CPU resources and might reduce performance. WARNING: Some character data was lost during transcoding in the dataset DATA.COX. Either the data contains characters that are not representable in the new encoding or truncation occurred during transcoding. NOTE: Line generated by the macro variable "COVLIST". 1 %_list(sex1 age1 comorbidities1 comorbidities2 comorbidities3 comorbidities4 comorbidities5     -   22   200 1 ! comorbidities6 concurrent1 concurrent2 concurrent previous after) WARNING: Apparent invocation of macro _LIST not resolved. ERROR 22-322: Syntax error, expecting one of the following: a name, a quoted string, a numeric constant, a datetime constant, a missing value, (, (|, ), *, ',', -, =, [, |, }. ERROR 200-322: The symbol is not recognized and will be ignored. NOTE: The data set WORK.INDATA has 979 observations and 3 variables. NOTE: Module MAIN is undefined in IML; cannot be RUN. NOTE: Exiting IML. NOTE: PROCEDURE IML used (Total process time): real time 0.07 seconds cpu time 0.06 seconds   NOTE: There were 979 observations read from the data set WORK.INDATA. NOTE: The data set WORK.INDATA has 979 observations and 3 variables. NOTE: PROCEDURE SORT used (Total process time): real time 0.03 seconds cpu time 0.03 seconds   NOTE: There were 979 observations read from the data set WORK.INDATA. NOTE: The data set WORK.ALLTIME has 166 observations and 1 variables. NOTE: DATA statement used (Total process time): real time 0.01 seconds cpu time 0.03 seconds   NOTE: There were 166 observations read from the data set WORK.ALLTIME. NOTE: The data set WORK.ALLTIME has 166 observations and 1 variables. NOTE: PROCEDURE SORT used (Total process time): real time 0.01 seconds cpu time 0.01 seconds   NOTE: There are no explanatory variables in the MODEL statement. NOTE: Convergence criterion (GCONV=1E-8) satisfied. NOTE: The data set WORK.BEST has 1 observations and 5 variables. NOTE: The data set WORK.COXOUT has 979 observations and 4 variables. NOTE: PROCEDURE PHREG used (Total process time): real time 0.10 seconds cpu time 0.03 seconds   NOTE: There were 979 observations read from the data set WORK.COXOUT. NOTE: The data set WORK.COXOUT has 979 observations and 4 variables. NOTE: PROCEDURE SORT used (Total process time): real time 0.01 seconds cpu time 0.00 seconds   NOTE: There were 1 observations read from the data set WORK.BEST. NOTE: The data set WORK.BEST has 1 observations and 5 variables. NOTE: The data set WORK.SIGMA has 0 observations and 5 variables. NOTE: DATA statement used (Total process time): real time 0.03 seconds cpu time 0.01 seconds ERROR: %EVAL function has no expression to evaluate, or %IF statement has no condition. ERROR: The %TO value of the %DO GROUP loop is invalid. ERROR: The macro _ADJSURV will stop executing. ... View more