This is the SGPLOT result so far: And the LIFETEST plot already has the medians and censored summary. I'm thinking the LIFETEST KM plot is closer to being done than the SGPLOT KM plot, yes? If you can tell me what you want excluded from the LIFETEST plot, and exactly what you want added and where, I might be able to provide code.  ... View more

Depending on what you want, you might be able to do the customizations directly in PROC LIFETEST, using the macros.   But you can save the Quartiles and the CensoredSummary ODS output data sets in order to capture the data in the added table. I used the %SurvivalSummaryTable to add the Number of Patients, etc. in the example below:   ods path(prepend) work.templat(update); data _null_;   %let url = //support.sas.com/documentation/onlinedoc/stat/ex_code/151;   infile "http:&url/templft.html" device=url;   file 'macros.tmp';   retain pre 0;   input;   _infile_ = tranwrd(_infile_, '&amp;', '&');   _infile_ = tranwrd(_infile_, '&lt;' , '<');   if index(_infile_, '</pre>') then pre = 0;   if pre then put _infile_;   if index(_infile_, '<pre>') then pre = 1; run; %inc 'macros.tmp' / nosource; %ProvideSurvivalMacros %SurvivalSummaryTable %CompileSurvivalTemplates data bmt;   set sashelp.bmt;   xval = 250;   yval = 0.2; run; ods select SurvivalPlot; proc lifetest data=bmt plots=(survival(cb=hw atrisk=0 to 2500 by 500));   time T*status(0);   strata group /test=logrank adjust=sidak;   ods output Quartiles=Quartiles CensoredSummary=Summary; run;   proc print data=Quartiles;   where percent=50; run; proc print data=Summary; run;   ... View more

You would use SURVEYREG with the default VARMETHOD=TAYLOR. ... View more

PROC SURVEYIMPUTE generates imputation-adjusted replicate weights that you then use in another survey procedure. This is different than a multiply imputed data set, so you do not need to use PROC MIANALYZE. Instead, read the imputed data into PROC SURVEYREG and specify the replication method for variance estimation using the VARMETHOD= option. The imputation variance will be accounted for. The Getting Started example in PROC SURVEYIMPUTE explains it well. ... View more

Sorry about the typo!    Try adding the statement   ODS _ALL_ CLOSE;   at the beginning of your program. If you are specifying an ODS destination, with an ODS PDF or ODS RTF statement, for example, you might need to modify the syntax there as well. I'm not an expert in this area, so if this doesn't work, include your full code or log and someone else might weigh in, or you could contact Technical Support. ... View more

Here's one approach: proc univariate data=sashelp.class noprint;   class sex;   histogram height/ nobars normal(noprint);   ods ouput histogram=hist; run; data hist;   set hist;   lower=0; run; proc sgplot data=hist;   series x=CurveX y=CurveY/group=Class1;   band x=CurveX lower=lower upper=CurveY/group=Class1 transparency=.5; run;     ... View more

If you still need it... you can modify the plot template to customize the y-axis range in a CIF plot.    Here is a default plot:   ods select CIFPlot; proc phreg data=Bmt plots(overlay)=cif;   class Disease;   model T*Status(0)=Disease / eventcode(cox)=1;   baseline covariates=Risk; run;     You can add the following LinearOpts= in the YaxisOpts option to specify your own y-axis range:   proc template;   define statgraph Stat.PHReg.Graphics.Cif;     dynamic title1 title2 title3 title4 xviewMin xviewMax group groupIndex       groupName plotCL transparency _byline_ _bytitle_ _byfootnote_;     BeginGraph;       entrytitle TITLE1 TITLE3;       entrytitle TITLE2 TITLE4 / textattrs=GRAPHVALUETEXT;       layout overlay / xaxisopts=(linearopts=(viewmin=XVIEWMIN viewmax=        XVIEWMAX)) yaxisopts=(label="Probability"        linearopts=(viewmin=0 viewmax=1 tickvaluelist=(0 .2 .4 .6 .8 1.0)));        if (PLOTCL)         bandplot LimitLower=LOWERCIF LimitUpper=UPPERCIF x=TIME / group           =GROUP index=GROUPINDEX modelname="CIF" datatransparency=           transparency;        endif;       stepplot y=CIF x=TIME / group=GROUP index=GROUPINDEX name="CIF";       if (EXISTS(GROUP))         discretelegend "CIF" / location=outside title=GROUPNAME;       endif;     endlayout;     if (_BYTITLE_)       entrytitle _BYLINE_ / textattrs=GRAPHVALUETEXT;     else       if (_BYFOOTNOTE_)         entryfootnote halign=left _BYLINE_;      endif;     endif;     EndGraph;   end; run; ods select CIFPlot; proc phreg data=Bmt plots(overlay range=0,500)=cif;   class Disease;   model T*Status(0)=Disease / eventcode(cox)=1;   baseline covariates=Risk; run;   Delete the modified template to revert back to the default plot:   proc template;   delete Stat.PHReg.Graphics.Cif; run;     ... View more

The E option in the ESTIMATE statement prints the L matrix, so that you can confirm your specification is estimating what you want it to. The JOINT option is what actually performs the joint test. The 1’s are the coefficients for the estimate you want. If you are unfamiliar with the concepts and syntax for writing contrasts and estimates, you will need to do a bit of homework. There is good SAS Note and many posts here in the Communities.   Yes, the joint test result is the F test at the end. It’s easier to navigate the output if you add labels in your ESTIMATE statements. Such as:      estimate “Joint test of A and B” a 1, b 1 / joint e;   You don’t have an interaction effect in your model, so the model results are the same. They are not affected by ESTIMATE statements. Your estimate is non-estimable, though. Since you have a 2-level class var, I believe this is what you want, but I have not confirmed it: estimate homa_ir 1, demo_age 1 -1 /joint e; ... View more

Joint F tests in SURVEYPHREG are done with the JOINT option in the ESTIMATE statement. Here is an example, written by a colleague:   The example below constructs a joint F test for A and B. The E option in the ESTIMATE statement prints the L matrix.   proc surveyphreg data = dset;    weight wt;    strata str;    cluster id;    model time*status(0) = a b;    estimate a 1, b 1 / joint e; run;   If a and b are the only two covariates in the model, then you can use the test from the “Testing Global Null Hypothesis” table. Test for the global null is computed by default. In addition, PROC SURVEYPHREG supports a few degrees of freedom adjustment methods for testing the global null. ... View more

The log message is a Note and not an Error. When you have clusters and strata, clusters are nested within strata in the SAS survey procedures. If there is a stratum with only a single cluster in it, the within-stratum variance cannot be computed. (You can look at the variance estimation formula and see where (n_h - 1) introduces a division by zero). The message is letting you know this stratum is omitted from the variance calculation, treating its contribution as zero. The results are valid, but if you don't want to make the zero assumption, you would need to make prior adjustments to your data such as collapsing strata. ... View more

It looks like there is a hard-coded limit of 16 characters in the procedure. You will need to make your ID values unique in the first 16 characters to be able to differentiate them. You can report the issue to Technical Support, and request that the 16 character limit be extended. ... View more