Hello, I had previously asked about plotting Proc PHREG for restricted splines analysis and was able to use this code: proc phreg data = have;
title "Colorectal-cancer incidence attempt";
effect resid_total_polys = spline(resid_total_poly / basis=tpf(noint) NATURALCUBIC details knotmethod=Percentilelist(5 35 65 95));
class menopause_with_males (ref="0") race (ref="1") hhincome (ref="1")
enrollment_source (ref="C") fh_colorectalcancer (ref="0")
BMI_category_expanded (ref="0") alcohol_category (ref="0") smokestatus_packyear (ref="0") hei10_category (ref="0");
model eof_age_CRC*Inc_CRC(0) = resid_total_polys
menopause_with_males race hhincome enrollment_source
BMI_category_expanded totalactivitymethr smokestatus_packyear Comorbidity_Index
ffq_kcal hei10_category alcohol_category fh_colorectalcancer
/ entry=enrollment_agemonths rl=wald;
store work.coxr;
run;
%macro est(ref=212, start=0, end=3000, by=1);
%Do i = 1 %To %eval(%SysFunc( Ceil( %SysEvalF( ( &End - &Start ) / &By ) ) ) +1) ;
%Let value=%SysEvalF( ( &Start - &By ) + ( &By * &I ) ) ;
estimate "&value." resid_total_polys [-1, &ref] [1, &value] / exp cl;
%end;
%mend est;
ods exclude all;
ods dataset Estimates=Estimates;
proc plm restore=work.coxr;
%est(ref=212, start=0, end=3000, by=1);
run;
ods exclude none;
data estimates;
set estimates;
resid_total_poly=label*1;
run;
proc sgplot data=estimates NOAUTOLEGEND Noborder;
Series y=ExpEstimate x=resid_total_poly / LINEATTRS=(color=black thickness=3px);
Series y=LowerExp x=resid_total_poly / LINEATTRS=(pattern=ShortDash color=Black THICKNESS=1);
Series y=UpperExp x=resid_total_poly / LINEATTRS=(pattern=ShortDash color=Black THICKNESS=1);
band x=resid_total_poly upper=UpperExp lower=LowerExp / fillattrs=(color=graydd) transparency=.50;
REFLINE 1 / axis=y;
REFLINE 3000 / Axis=X LINEATTRS=(pattern=ThinDot color=Black THICKNESS=1);;
yaxis Values=(0.5 0.8 1 1.2 1.5) Label="Hazard ratio" Type=LOG LABELATTRS=(weight=BOLD);
xaxis min=0 VALUES=(0 to 3000 by 100) LABELATTRS=(weight=BOLD);
run; To produce this graph: Now, however, I am using multiple imputation to handle missing values, but I am not sure how to adapt the code to be able to handle this. This is the code I have: proc phreg data = mult_imp.mi_mvn namelen=50;
title "All-cancer incidence attempt";
effect resid_total_polys = spline(resid_total_poly / basis=tpf(noint) NATURALCUBIC details knotmethod=Percentilelist(5 35 65 95));
model eof_age_CRC*Inc_CRC(0) = resid_total_polys
menopause_males_dum2 menopause_males_dum3 racedum2 racedum3 incomedum2 incomedum3 incomedum4
enrolldum2 BMI_dum2 BMI_dum3 totalactivitymethr packyears_dum2 packyears_dum3 packyears_dum4 packyears_dum5 Comorbidity_Index
ffq_kcal hei10_category alcohol_dum2 alcohol_dum3 fh_colorectalcancer
/ entry=enrollment_agemonths rl=wald;
by _imputation_;
store work.coxr;
run;
quit;
%macro est(ref=212, start=0, end=3000, by=1);
%Do i = 1 %To %eval(%SysFunc( Ceil( %SysEvalF( ( &End - &Start ) / &By ) ) ) +1) ;
%Let value=%SysEvalF( ( &Start - &By ) + ( &By * &I ) ) ;
estimate "&value." resid_total_polys [-1, &ref] [1, &value] / exp cl;
%end;
%mend est;
ods exclude all;
ods dataset Estimates=Estimates;
proc plm restore=work.coxr;
%est(ref=212, start=0, end=3000, by=1);
run;
ods exclude none;
data estimates;
set estimates;
resid_total_poly=label*1;
run;
proc sgplot data=estimates NOAUTOLEGEND Noborder;
Series y=ExpEstimate x=resid_total_poly / LINEATTRS=(color=black thickness=3px);
Series y=LowerExp x=resid_total_poly / LINEATTRS=(pattern=ShortDash color=Black THICKNESS=1);
Series y=UpperExp x=resid_total_poly / LINEATTRS=(pattern=ShortDash color=Black THICKNESS=1);
band x=resid_total_poly upper=UpperExp lower=LowerExp / fillattrs=(color=graydd) transparency=.50;
REFLINE 1 / axis=y;
REFLINE 3000 / Axis=X LINEATTRS=(pattern=ThinDot color=Black THICKNESS=1);;
yaxis Values=(0.5 0.8 1 1.2 1.5) Label="Hazard ratio" Type=LOG LABELATTRS=(weight=BOLD);
xaxis min=0 VALUES=(0 to 3000 by 100) LABELATTRS=(weight=BOLD);
run; Where the set "mult_imp.mi_mvn" is the dataset obtained after running Proc MI with 5 imputations. I am stuck trying to figure out how to get a single graph for this model that combines the 5 imputed sets. However, this produces this plot: I was thinking that I somehow need to use proc mianalyze to combine the estimates for the models from each imputation but was not sure how to accomplish this.
... View more
