1. Grant Brown

2.  AIDS patients – compliance with treatment  Binary response – complied or no  Attempt to find factors associated with better compliance.  Seems a natural choice for logistic regression

3.  Variable coding was problematic  Entirely composed of factors, many included.  Client had convergence problems.  Many ‘cells’ were empty, so no MLE could be found.

4.  Very simple to check where the errors are coming from, simply perform a proc freq on the factors in your model.  Start with simple frequencies to check for any extremely skewed groups.  Move on to cross-tab frequencies, which give lots of output, but show each cell. proc freq data=dataset; tables var1* var2 * var3; run; proc freq data=dataset; tables var1 var2 var3; run;

5.  Relational databases are ubiquitous.  Keys  Identifier fields ▪ Numbers ▪ Dates  Examples  Problems  Everything.  Databases grow over time, so problems can compound.

6.  The syntax is deceptively simple.  Issues:  Sorting  Catching non matched cases  Many-to-many woes  General advice:  Understand input datasets  Verify output datasets … merge DataSet1 DataSet2; by SomeVar1 SomeVar2; …

7.  Combines ‘uniquely’ matched records.  Matched by combination of ‘by’ variables.  Data set on the right side of the statement wins in cases of conflict.  If you need to understand it’s behavior more specifically, you are probably doing something wrong. (multiple instances of by variables in both data sets)

8. DATA Merged NotMerged1 NotMerged2; MERGE data1 (IN = in1) data2 (IN = in2); BY var1 var2 var3; IF (in1 AND in2) then output Merged; ELSE IF (in1 AND NOT in2) THEN OUTPUT NotMerged1; ELSE IF (in2 AND NOT in1) THEN OUTPUT NotMerged2; RUN; PROC SORT DATA = data1; BY var1 var2 var3; RUN; PROC SORT DATA = data2; BY var1 var2 var3; RUN;

9. DATA newdata; MERGE data1 data2; BY var1; RUN;

10.  Limit your input data sets to ‘by’ fields and information needed in the output before the merge.  Spot check your results.  Count observations in the logs.  Create a ‘unique’ variable beforehand if you are getting mysterious records.  If you are doing the same thing over and over and just changing variable names, spend 10 minutes looking at macro examples.