Hi everyone! I’m working on the causal mediation analysis. Because of missing data in my study variables, I chose multiple imputation to generate 5 completed datasets. After finishing the causalmed analysis of all dataset. I found it difficult to pool the results together. The examples given in the sashelp use ‘ods output parameterestimates = result’ to generate the result for MIANALYZE. But when I put same syntax in sas, a warning occurred listed below.   WARNING: Output 'parameterestimates' was not created. Make sure that the output object name, label, or path is spelled correctly. Also, verify that the appropriate procedure options are used to produce the requested output object. For example, verify that the NOPRINT option is not used.   I’ve tried to replace ‘ParameterEstimates’ with ‘MediatorEstimates’. The result stayed same. Is there any code that could generate parameter estimates in proc causalmed that could be used in MIANALYZE? Many thanks!   My code proc mi data = pat nimpute = 5 out = newpat5 minimum = 0 seed = 309943001; fcs regpmm(leptin) outiter = para5; var hwbmi vsage1 cvd leptin; run; proc causalmed data = newpat5; class status(ref = first) drink smoke bmi cvd; model status = event leptin; mediator leptin = event; covar drink smoke bmi cvd; bootstrap CI (PERC) Nboot = 1000; by _Imputation_; ods output parameterestimates = result; run; ... View more

Code below is the example from SAS GUIDE. I am working on a competing risk survival analysis. But this example only has one covariate affecting the event. But my model has more than one categorical variables. How can I create dataset ("risk" in my example) in the "baseline covariates =" syntax to plot CIF plot according to the categorical variable "class5"? All the variables are categorical except Age. Thanks very much!   /*My code*/ proc phreg data=pat plots(overlay) = cif; class class5(order = internal ref=first) bmi race drink smoke cvd; model time*status(0) = class5 age bmi race drink smoke cvd / risklimits = WALD eventcode=1; baseline covariates = risk / rowid = class5; run;   /*Example from SAS guide*/ proc format; value DiseaseGroup 1='ALL' 2='AML-Low Risk' 3='AML-High Risk';   data Bmt; input Disease T Status @@; label T='Disease-Free Survival in Days'; format Disease DiseaseGroup.; datalines; 1 2081 0 1 1602 0 1 1496 0 1 1462 0 1 1433 0 1 1377 0 1 1330 0 1 996 0 1 226 0 1 1199 0 1 1111 0 1 530 0 1 1182 0 1 1167 0 1 418 2 1 383 1 1 276 2 1 104 1 1 609 1 1 172 2 1 487 2 1 662 1 1 194 2 1 230 1 1 526 2 1 122 2 1 129 1 1 74 1 1 122 1 1 86 2 1 466 2 1 192 1 1 109 1 1 55 1 1 1 2 1 107 2 1 110 1 1 332 2 2 2569 0 2 2506 0 2 2409 0 2 2218 0 2 1857 0 2 1829 0 2 1562 0 2 1470 0 2 1363 0 2 1030 0 2 860 0 2 1258 0 2 2246 0 2 1870 0 2 1799 0 2 1709 0 2 1674 0 2 1568 0 2 1527 0 2 1324 0 2 957 0 2 932 0 2 847 0 2 848 0 2 1850 0 2 1843 0 2 1535 0 2 1447 0 2 1384 0 2 414 2 2 2204 2 2 1063 2 2 481 2 2 105 2 2 641 2 2 390 2 2 288 2 2 421 1 2 79 2 2 748 1 2 486 1 2 48 2 2 272 1 2 1074 2 2 381 1 2 10 2 2 53 2 2 80 2 2 35 2 2 248 1 2 704 2 2 211 1 2 219 1 2 606 1 3 2640 0 3 2430 0 3 2252 0 3 2140 0 3 2133 0 3 1238 0 3 1631 0 3 2024 0 3 1345 0 3 1136 0 3 845 0 3 422 1 3 162 2 3 84 1 3 100 1 3 2 2 3 47 1 3 242 1 3 456 1 3 268 1 3 318 2 3 32 1 3 467 1 3 47 1 3 390 1 3 183 2 3 105 2 3 115 1 3 164 2 3 93 1 3 120 1 3 80 2 3 677 2 3 64 1 3 168 2 3 74 2 3 16 2 3 157 1 3 625 1 3 48 1 3 273 1 3 63 2 3 76 1 3 113 1 3 363 2 ;   data Risk; Disease=1; output; Disease=2; output; Disease=3; output; format Disease DiseaseGroup.; run;   ods graphics on; proc phreg data=Bmt plots(overlay=stratum)=cif; class Disease (order=internal ref=first); model T*Status(0)=Disease / eventcode=1; Hazardratio 'Pairwise' Disease / diff=pairwise; baseline covariates=Risk out=out1 cif=_all_ / seed=191; run; ods graphics on; title 'Cause-specific Analysis'; proc phreg data=Bmt plots(overlay)=cif; class Disease (order=internal ref=first); model T*Status(0)=Disease / eventcode(cox)=1; baseline covariates=Risk out=out2 cif=_all_; run; ... View more