I am exporting from SAS to Excel (CSV).  I have three-digit subject numbers such as 001, 002, 003, etc.  When I run the PROC EXPORT, I get 1, 2, 3, etc. for that field.  Is there an option I can specify to keep it as a three-digit character string in Excel? ... View more

I have a dataset containing several observations.  I am interested in looking at just 6 subjects to see how they influence the results of a Variable1 if I change it to be the value in Variable2.  I want to change the value of one subject at a time (6 passes) then change 2 subjects at a time (15 passes) then 3 subjects at a time (20 passess), etc. up to changing all 6 patients.  I know the combinations that the patients make (a total of 63 passess), but it is not as easy as setting up 6 nested do-loops because that would run far too many iterations.  I hope this makes sense.  Any suggestions on how to go about this? Here is a very small example.  Suppose I have 5 subjects, and I am only interested in doing what I described above to 2 of them (a total of 6 passes).  I want to change the subjects value from Var1 to Var2. Original Data: Subject Var1 Var2 1 7 4 2 10 15 3 3 9 4 2 2 5 9 9 Change 1 subject at a time: Change subject 1 Subject Var1 Var2 1 4 4 2 10 15 3 3 9 4 2 2 5 9 9 Change subject 2 Subject Var1 Var2 1 7 4 2 15 15 3 3 9 4 2 2 5 9 9 Change subject 3 Subject Var1 Var2 1 7 4 2 10 15 3 9 9 4 2 2 5 9 9 Change 2 subjects at a time: Change subjects 1&2 Subject Var1 Var2 1 4 4 2 15 15 3 3 9 4 2 2 5 9 9 Change subjects 1&3 Subject Var1 Var2 1 4 4 2 10 15 3 9 9 4 2 2 5 9 9 Change subjects 2&3 Subject Var1 Var2 1 7 4 2 15 15 3 9 9 4 2 2 5 9 9 ... View more

I know how to use the RETAIN statement to fill in missing values with the last non-missing value, but is there a way to do it backwards?  That is, can I fill in previous missing values with the next non-missing value? Example - I am trying to get "Filled Data" from the "Missing Values" column: Time Point Missing Values Filled Data 1 . 7 2 . 7 3 . 7 4 . 7 5 7 7 6 . 6 7 6 6 8 5 5 9 . 4 10 4 4 11 3 3 12 2 2 13 1 1 ... View more

I know that SAS can do this with PROC LIFETEST, but I want to attempt this by hand.  Even though I can easily do this in Excel or R, this might be a good learning experience.  I am essentially trying to reproduce the product-limit table for some survival data.  Below is a look at how my data currently looks.  The value in "AtRisk" is a macro variable placed in the dataset if it is the first observation. What I want to do is calculate AtRisk(i) = AtRisk(i-1)-censored(i-1)-event(i-1).  In other words, AtRisk for SurvivalTime=2 is 49-5-0=44.  Then At Risk for SurvivalTime=2 is 44-0-1=43, etc. I have tried using the RETAIN statement and the lag() function to perform this, but I cannot get either one to work.  If you have any better ideas, please help me out! Here is an example of my code that does not do the trick: data data2;      set data1;      by _LABEL_;     /* NOTE: _LABEL_ has the same value throughout the data */      if first._LABEL_ then atrisk=&atrisk.;            else atrisk=lag(atrisk); run; CURRENT DATA: _NAME_ SurvivalTime Censorred Event AtRisk COUNT 1 5 0 49 COUNT 2 0 1 . COUNT 3 0 1 . COUNT 4 1 0 . COUNT 5 0 1 . ... ... ... ... ... CORRECT DATA: SurvivalTime Censored Event AtRisk 1 5 0 49 2 0 1 44 3 0 1 43 4 1 0 42 5 0 1 41 ... ... ... ... ... View more

I am attempting to make a "waterfall" plot with the NEEDLE interpolation in PROC GPLOT.  My plot is divided into two different groups.  I wanted the positive group to be blue and the negative group to be red.  Here is what I have attempted: axis1 label=(angle=90 'Best Percent Change in Lesion Size') order=(-100 to 150 by 50) minor=(n=4); axis2 label=(' ') value=none major=none minor=none; proc gplot data=waterfall;      plot ploty1*n ploty2*n / overlay vaxis=axis1 haxis=axis2;      symbol1 interpol=Needle value=none width=4 color=blue;      symbol2 interpol=Needle value=none width=4 color=red; run; I am not at all familiar with annotations, but I am sure I could do something with that.  I just have no idea where to start. This is not my full data, but here is an idea of how it looks: brpercent posneg n ploty1 ploty2 2.564 Positive 1 2.564 2.083 Positive 2 2.083 1.333 Positive 3 1.333 0 Positive 4 0 -5.263 Positive 5 -.263 -30 Positive 6 -30 -31.429 Positive 7 -31.429 -36.842 Positive 8 -36.842 -40 Positive 9 -40 -50 Positive 10 -50 150 Negative 11 150 142.857 Negative 12 142.857 78.152 Negative 13 78.152 58.065 Negative 14 58.065 52.174 Negative 15 52.174 -3.704 Negative 16 -3.704 -4 Negative 17 -4 -4.762 Negative 18 -4.762 -7.143 Negative 19 -7.143 -18.75 Negative 20 -18.75 ... View more