I also want to learn if there any statement to generate spline for odd ratios in SAS? ... View more

Hmmm, thanks. The EFFECTPLOT statement is powerful. However, I don't understand one thing.   proc logistic data=d202ps_3; class obesity_status(REF='0') genderC(REF='0') AF(REF='0') Hypertension(REF='0') CHF(REF='0') akiC(ref='0') ddiC(REF='0') indicationC(REF='0') CYP2C9code(REF='0') VKORC1Code(REF='0'); model firstInrAttain(event='1') = obesity_status Weight__kg_ ageY genderC AF Hypertension CHF serumAlb akiC ddiC indicationC CYP2C9code VKORC1Code / clodds=both clparm=both; effectplot slicefit(x=ageY sliceby=obesity_status=0 1 2) / clm noobs; run; and proc logistic data=d202ps_3; class /*obesity_status(REF='0')*/ genderC(REF='0') AF(REF='0') Hypertension(REF='0') CHF(REF='0') akiC(ref='0') ddiC(REF='0') indicationC(REF='0') CYP2C9code(REF='0') VKORC1Code(REF='0'); model firstInrAttain(event='1') = obesity_status Weight__kg_ ageY genderC AF Hypertension CHF serumAlb akiC ddiC indicationC CYP2C9code VKORC1Code / clodds=both clparm=both; effectplot slicefit(x=ageY sliceby=obesity_status=0 1 2) / clm noobs; run; The two produced obviously different graphs. The only difference in the code is whether obesity_status was listed as class/category variable. Also, if I treat obesity_status as a class variable, I cannot define its levels in the EFFECTPLOT analysis (i.e., it has 4 levels but I prefer to use only three levels.) Thanks. ... View more

Hello, everyone   Today I learned the PROC TRANSREG could be used to draw spline, e.g.,   ods graphics on; proc transreg data=d202ps_4; model identity(addMg) = spline(serumAlb); run; However, my linear regression model should include many other independent variables. I tried to add them to the PROC TRANSREG but it did not work as I expected. Does anyone know how to solve this issue (spline with other variables adjusted)?   Thanks ... View more

Oh, great. Let met study it. Thank you, sir.   Hmmm, it seems that the example in the thread used simple poisson regression with only one independent variable. But in reality, we often have multiple independent variables.   Tom ... View more

However, the method described in this note is derived from a model that includes the interaction part. How to acquire the result of a same function (difference in difference) in a model that does not have the interaction part? Thank you ... View more

OK. Let me take a look. What does it mean if the regression without interaction parts display significance for a parameter, and later another regression having interactions parts with that parameter lost this significance for that parameter?   For example, Y = beta0 + beta1 X1 + beta2 X2, showing beta1 is not zero. Later, Y = beta0 + beta1 X1 + beta2 X2 + beta3 X1*X2, showing beta2 is zero while beta3 is not zero. ... View more

PROC GLMSELECT DATA=WORK.D201; MODEL Average_daily_dose_during_the_in=Age__y_ Gender_code BSA AF Hypertension CHF Hypoalbuminemia_code AKI_for_T_test Potential_amiodarone_DDI T_test___indication var33 var34 / SELECTION=stepwise(SELECT=sl SLENTRY=0.20 SLSTAY=0.05 CHOOSE=adjrsq); STORE WORK.DOSEMODEL / LABEL='Linear Regression'; RUN; %PUT=&_GLSIND; PROC GLM DATA=WORK.D201; MODEL Average_daily_dose_during_the_in=&_GLSIND / solution CLPARM; RUN; PROC PLM RESTORE=WORK.DOSEMODEL; SCORE DATA=WORK.PREDICTED out=WORK.NEWDOSE predicted lclm uclm lcl ucl stderr / ALPHA=0.05; RUN; Thanks for your reply, Miller. Above is my code.   The two independent variables I am interested are AKI_for_T_test and var34. I want to test the interaction between the two but I don't like to use the multiplicative interaction variable (i.e., AKI_for_T_test*var34). Rather, I would like to use the additive interaction parameter. Both AKI_for_T_test and var34 can only be either 0 or 1. So there are four possible combinations, corresponding to Y11, Y01, Y10, and Y00. Y is the dependent variable Average_daily_dose_during_the_in.   ... View more