I tried a new model with two terms in it. Please see my code, log, and output for each approach. Code – proc genmod data = data descending; class var_a(ref='0') var_b(ref='0')/param=ref; model outcome(ref='0') = var_a var_b/ dist = binomial link = log type3; estimate 'Beta' var_a 1 -1/ exp; estimate 'Beta' var_b 1 -1/exp; run; Log – NOTE: PROC GENMOD is modeling the probability that outcome='1'. NOTE: The Pearson chi-square and deviance are not computed since the AGGREGATE option is not specified. NOTE: Algorithm converged. NOTE: The scale parameter was held fixed. WARNING: More coefficients than levels specified for effect var_a. Some coefficients will be ignored. WARNING: More coefficients than levels specified for effect var_b. Some coefficients will be ignored. NOTE: PROCEDURE GENMOD used (Total process time): real time 1.15 seconds cpu time 0.78 seconds output - using nlmeans approach proc logistic data=data; class var_a(ref="0") var_b(ref="0")/ param=glm; model outcome(event="1")=var_a var_b; lsmeans var_a/ e ilink; lsmeans var_b/ e ilink; ods output coef=coeffs; store out=ques; run; %NLMeans(instore=ques, coef=coeffs, link=logit, options=ratio, title=Relative Risk); Log – NOTE: PROC LOGISTIC is modeling the probability that outcome='1'. NOTE: Convergence criterion (GCONV=1E-8) satisfied. NOTE: The LOGISTIC procedure generated the model item store WORK.QUES. NOTE: The data set WORK.COEFFS has 10 observations and 6 variables. NOTE: There were 341 observations read from the data set WORK.DATA. NOTE: PROCEDURE LOGISTIC used (Total process time): real time 2.42 seconds cpu time 1.62 seconds 2633 2634 %NLMeans(instore=ques, coef=coeffs, link=logit, options=ratio, title=Relative Risk); WARNING: The final Hessian matrix is not positive definite, and therefore the estimated covariance matrix is not full rank and may be unreliable. The variance of some parameter estimates is zero or some parameters are linearly related to other parameters. WARNING: The final Hessian matrix is not positive definite, and therefore the estimated covariance matrix is not full rank and may be unreliable. The variance of some parameter estimates is zero or some parameters are linearly related to other parameters. NOTE: The NLMEANS macro used 3.73 seconds. output - I am supposed to write separate LSMEANS statement and one macro statement, right? What am I doing wrong here? ... View more

oops just saw your second post. let me try that. thank you! ... View more

thanks for the quick response.   I think I will just run my model (that has more than one term) with proc logistic and convert odds ratio to relative risk. For models with one term, I will use the NLmeans approach. It would have been nice to use the same approach for models with multiple terms. ... View more

any idea how to run two or more terms in one model using this macro? thank you! ... View more

OK so I was running something wrong actually. I was modeling wrong event in one method. I am finally getting same RR for the two methods. However, 95% CL is different for each. NLmeans macro is giving me tighter bounds. Any opinion on why would this be? ... View more

I tried one of the terms, let's say term a by itself using proc logistic and then running nlmixed macro. I also ran log-binomial model using proc genmod. Relative risk that I am getting from proc genmod is vastly different from the one I get using nlmixed approach. That should not be the case, right? I can understand if it off by decimals but proc genmod is giving me almost double the RR of the other approach. Am I missing something? ... View more

I believe I already tried that and it did not work.   ... View more

I am attempting this approach. Unfortunately NLmeans macro is not working at the moment. I will keep at it and see what I get. Thank you! ... View more

Can I modify proc genmod in a way so that I can run it same as proc hpgenselect? I want to generate RR estimates off of this model but I can only get coefficients from proc hpgenselect which I will have to turn into RR estimates I want. if I can run this same model in proc genmod, then I can use estimate statement and get RR estimates. or if you have a different solution, that is also fine. ... View more

proc genmod data = data descending; class a(ref='0') b(ref='Non-Blast') c(ref='0') d(ref='0')/param=ref; model outcome(ref='No') = a b c d / dist = binomial link = log; run; Error I get from this is: NOTE: PROC GENMOD is modeling the probability that outcome='YES'. WARNING: The specified model did not converge. NOTE: The Pearson chi-square and deviance are not computed since the AGGREGATE option is not specified. ERROR: The mean parameter is either invalid or at a limit of its range for some observations. NOTE: The scale parameter was held fixed. NOTE: The SAS System stopped processing this step because of errors. NOTE: PROCEDURE GENMOD used (Total process time): real time 0.09 seconds cpu time 0.06 seconds   As compared to proc hpgenselect: PROC HPGENSELECT data=data; CLASS a(ref='0') b(ref='Non-Blast') c(ref='0') d(ref='0')/param=ref; model outcome(ref='No')=a b c d /dist=binomial link=log; RUN; log shows - NOTE: The HPGENSELECT procedure is executing in single-machine mode. NOTE: You are modeling the probability that outcome='YES'. NOTE: Convergence criterion (FCONV=2.1073424E-8) satisfied. NOTE: There were 341 observations read from the data set WORK.DATA. NOTE: PROCEDURE HPGENSELECT used (Total process time): real time 0.09 seconds cpu time 0.04 seconds   Am I doing something incorrect? ... View more