Thank you @Norman21 fro the help.    I managed to write the following code and it seems to work/provide the desired output.    Firstly, I created an empty table with the necessary specification before the macro as follows:  proc sql; Create table Datasets.Special_Perc_&Split (Variable char length = 23 format=$50., Sum_Special num length = 8 format = best12., Sum_positive num length = 8 format = best12., Percentage num length = 8 format = Percent6.2); quit;  Secondly,  I wrote a macro to calculate the percentage of special values for each column in any given data set.  This is then added to the table that I created above for each iteration of the loop.   %macro Special(dsn); %let lib = %upcase(%scan(&dsn,1,.)); %let dsn = %upcase(%scan(&dsn,2,.)); /* Creatign macro's for each column in the data set */ data _null_; set sashelp.vcolumn(keep=libname memname name type where=(libname = "&lib" AND memname = "&dsn" and type = "num")) end=eof; call symputx('col'||strip(_n_),strip(name)); if eof then call symputx('max',strip(_n_)); run; %do i = 1 %to &max; proc sql noprint; select sum(&&col&i in (-1,-2,-3,-4,-5,-6,-7)) as NegS, sum(&&col&i not in (-1,-2,-3,-4,-5,-6,-7)) as Pos, round(calculated NegS/(calculated NegS + calculated Pos), .01) as Perct into :SN, :SP, :Perc from &lib..&dsn; quit; data b; Variable = "&&col&i"; Sum_Special = &sn; Sum_positive = &sp; Percentage = &Perc; run; proc append base = Datasets.Special_Perc_&split data = b force; run; proc sql; drop table b; quit; %end; %mend special; %special(Datasets.Longevity_Numeric_&split); Lastly, i use a proc sql to exclude those variables that have a special value percentage of more than 90%.    It may not be the best way to do it, but it got the job done.    ... View more

The Friedman test depends on the time factor being measured within subject, so I don't think that it is quite what would fit this design.  There is a test called the "aligned ranks transformation".  This link provides a SAS approach: https://libres.uncg.edu/ir/uncg/f/S_Richter_C463_2002.pdf    This will allow you to correctly test for a time by treatment interaction, although probably not as powerfully as a transformed or generalized linear model.   SteveDenham   PS Here is another paper that isn't so SAS-centric. https://www.researchgate.net/publication/221514774_The_Aligned_Rank_Transform_for_Nonparametric_Factorial_Analyses_Using_Only_ANOVA_Procedures  ... View more