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Hi Kathleen,   Thanks for your suggestions.  I tried it, but it turned out the estimate cannot be estimated. I believe that PROC GLIMMIX default is GLM coding for the class statement.  What you have suggested is for effect coding for class statement which to my understanding is not available in PROC GLIMMIX. ... View more

Hi Steve, Thanks for your suggestions. It does seem to correct the problem. However, one thing I noticed is that when the ref group (2018 vs 2012 as reference) is changed, the mean results output seem to be slightly different. Why is this? I figure it should be the same, no? proc glimmix data=newadr_ge200_avg_2012_v2 noclprint method=laplace; class nMD procyear; model adeno_yes(event=last)=procyear/cl dist=binary link=logit solution; random procyear/ sub=nMD type=ar(1) s CL; lsmeans procyear/ ilink e; run; HTML clipboard Solutions for Fixed Effects Effect procyear Estimate Standard Error DF t Value Pr > |t| Alpha Lower Upper Intercept -0.3822 0.1366 231 -2.80 0.0056 0.05 -0.6514 -0.1130 procyear 2012 -1.0368 0.1557 231 -6.66 <.0001 0.05 -1.3435 -0.7302 procyear 2013 -0.9708 0.1420 231 -6.84 <.0001 0.05 -1.2506 -0.6910 procyear 2014 -0.8767 0.1284 231 -6.83 <.0001 0.05 -1.1298 -0.6237 procyear 2015 -0.7011 0.1139 231 -6.16 <.0001 0.05 -0.9254 -0.4768 procyear 2016 -0.3416 0.09702 231 -3.52 0.0005 0.05 -0.5328 -0.1505 procyear 2017 -0.05344 0.07212 231 -0.74 0.4594 0.05 -0.1955 0.08865 procyear 2018 0 . . . . . . . HTML clipboard Coefficients for procyear Least Squares Means Effect procyear Row1 Row2 Row3 Row4 Row5 Row6 Row7 Intercept 1 1 1 1 1 1 1 procyear 2012 1 procyear 2013 1 procyear 2014 1 procyear 2015 1 procyear 2016 1 procyear 2017 1 procyear 2018 1 HTML clipboard procyear Least Squares Means procyear Estimate Standard Error DF t Value Pr > |t| Mean Standard Error Mean 2012 -1.4190 0.1513 231 -9.38 <.0001 0.1948 0.02374 2013 -1.3530 0.1447 231 -9.35 <.0001 0.2054 0.02362 2014 -1.2589 0.1405 231 -8.96 <.0001 0.2212 0.02420 2015 -1.0833 0.1375 231 -7.88 <.0001 0.2529 0.02597 2016 -0.7238 0.1364 231 -5.31 <.0001 0.3265 0.02999 2017 -0.4356 0.1350 231 -3.23 0.0014 0.3928 0.03221 2018 -0.3822 0.1366 231 -2.80 0.0056 0.4056 0.03294 However, if I put ref=first to indicate 2012 as reference group proc glimmix data=newadr_ge200_avg_2012_v2 noclprint method=laplace; class nMD procyear*(ref=first)*/; model adeno_yes(event=last)=procyear/cl dist=binary link=logit solution; random procyear/ sub=nMD type=ar(1) s CL; lsmeans procyear/ ilink e; run; HTML clipboard Solutions for Fixed Effects Effect procyear Estimate Standard Error DF t Value Pr > |t| Alpha Lower Upper Intercept -1.2370 0.1397 231 -8.85 <.0001 0.05 -1.5123 -0.9616 procyear 2013 -0.1178 0.1549 231 -0.76 0.4476 0.05 -0.4230 0.1873 procyear 2014 -0.02350 0.1440 231 -0.16 0.8705 0.05 -0.3072 0.2602 procyear 2015 0.1533 0.1328 231 1.15 0.2494 0.05 -0.1083 0.4149 procyear 2016 0.5146 0.1218 231 4.23 <.0001 0.05 0.2746 0.7545 procyear 2017 0.8057 0.1076 231 7.48 <.0001 0.05 0.5936 1.0178 procyear 2018 0.8671 0.09087 231 9.54 <.0001 0.05 0.6880 1.0461 procyear 2012 0 . . . . . . . HTML clipboard Coefficients for procyear Least Squares Means Effect procyear Row1 Row2 Row3 Row4 Row5 Row6 Row7 Intercept 1 1 1 1 1 1 1 procyear 2013 1 procyear 2014 1 procyear 2015 1 procyear 2016 1 procyear 2017 1 procyear 2018 1 procyear 2012 1 HTML clipboard procyear Least Squares Means procyear Estimate Standard Error DF t Value Pr > |t| Mean Standard Error Mean 2013 -1.3548 0.1439 231 -9.41 <.0001 0.2051 0.02347 2014 -1.2605 0.1390 231 -9.07 <.0001 0.2209 0.02392 2015 -1.0837 0.1354 231 -8.01 <.0001 0.2528 0.02557 2016 -0.7224 0.1337 231 -5.40 <.0001 0.3269 0.02941 2017 -0.4313 0.1313 231 -3.28 0.0012 0.3938 0.03135 2018 -0.3699 0.1313 231 -2.82 0.0053 0.4086 0.03172 2012 -1.2370 0.1397 231 -8.85 <.0001 0.2250 0.02437 ... View more

Hi Steve, I tried it as proc genmod data=workdata.all_combined; model adr=/dist=binomial link=logit; run; ADR = percentage (procedures with polyps/total procedures) I get the following error. ERROR: The response variable ADR has 150 levels. A binary response must have two levels. ERROR: No valid observations due to invalid or missing values in the response, explanatory, offset, frequency, or weight variable. NOTE: The SAS System stopped processing this step because of errors. NOTE: PROCEDURE GENMOD used (Total process time): real time 0.08 seconds What am I doing wrong here? ... View more

Hi Chris,   Thanks for your response. However, I am doing this in a data step and not under proc sql.   I have several other select-when statements in my data step, so I like to be able to do it in my data step. Is there a way to do this?   Example: Data two; set one; select; when (scope_number_or like "24%", scope_number_or like "25%", scope_number_or like "26%", scope_number_or like "%27") HDscope=0; when (scope_number_or like "N/A") HDscope=.; otherwise HDscope=1; end; run;   ... View more

Hi Chris,   Believe it or not.  I tried hhinohar's dosubl  It actually works. Well, at least one thing is for sure after this, I need to read up on how to better use SAS macro. Thanks for your help working through this.  My technique is ugly.  I wish it was more elegant.   ... View more