One option is to fit a logistic regression in the NLMIXED procedure and estimate the log(RR) via ESTIMATE statements. The brilliant Dale has explained how to do this previously. Search the archives. A log-binomial is possible via GENMOD or GLIMMIX but you could run into convergence problems or predicted probabilities greater than 1.0.

Ryan

On Mar 28, 2012, at 9:09 AM, "Kevin F. Spratt" <Kevin.F.Spratt@DARTMOUTH.EDU> wrote:

> I have run a glimmix procedure using poisson regression to estimate > relative risks > associated with various binary predictors. I show two models below. > > * full model; > > PROC GLIMMIX DATA=save.demos METHOD=RSPL; > CLAsS pT_age_c2 pt_sex pt_emp pt_ret smoke > diabe hp_card hp_renu hp_psyc ; > > MODEL dv = pt_age_c2 pt_sex pt_emp pt_ret > smoke diabe hp_card hp_renu hp_psyc / LINK=LOG > S DIST=POISSON; > > LSMEANS pt_age_c2 / ILINK CL OM PDIFF; > LSMEANS pt_sex / ILINK CL OM PDIFF; > LSMEANS pt_emp / ILINK CL OM PDIFF; > LSMEANS pt_ret / ILINK CL OM PDIFF; > LSMEANS smoke / ILINK CL OM PDIFF; > LSMEANS diabe / ILINK CL OM PDIFF; > LSMEANS hp_card / ILINK CL OM PDIFF; > LSMEANS hp_renu / ILINK CL OM PDIFF; > LSMEANS hp_psyc / ILINK CL OM PDIFF; > > run; > > * one variable model; > > PROC GLIMMIX DATA=save.demos METHOD=RSPL; > CLAsS pT_age_c2 ; > > MODEL dv = pt_age_c2 / LINK=LOG S DIST=POISSON; > > LSMEANS pt_age_c2 / ILINK CL OM PDIFF; > > run; > > My problem: > > For the one variable model, the results produce the RR that is > consistent with what > I get with proc freq, but the confidence intervals for the glimmix and freq > procedures are not even close. This is probably due to the fact that the > dependent variable (dv), although 0/1, is not necessarily a rare event. > > So, for the one variable models I can get my results using proc freq but for > the multi-variable model I plan to figure out a way to produce > non-parametric confidence > intervals using bootstrapping. Before I go to this trouble, I'm wondering if > there is a more straightforward way to do this within glimmix. Can I > changes some > options in the glimmix model that will "magically" solve this problem > or are the > model coefficients in the multi-variable Poisson model not able to > provide appropriate > adjusted relative risk estimates? > > I have run the models using logistic regression and get odds ratios, > but, as we know > odds ratios and relative risks can be quite different when the 0/1 > dependent variable > is not relative "rare." The differences in these data, at the > univariate level > suggest that ORs are markedly over-estimating variable effects > compared to the RRs. > > As always, any help greatly appreciated. > > PS: if the RR estimates are correct but bootstrapping to obtain > accurate non-parametric > confidence intervals for the values is needed, would this be a > reasonable paper to submit > for next year's SGF? If so, I would be willing to collaborate. I'm > sure I can figure > out how to do it, I'm less sure that I can produce a "really cool" > macro that would be > of use to the community. > > > > > ______________________________________________________________________ > > Kevin F. Spratt, Ph.D. > Department of Orthopaedic Surgery > Dartmouth Medical School > One Medical Center Drive > DHMC > Lebanon, NH USA 03756 > (603) 653-6012 (voice) > (603) 653-6013 (fax) > Kevin.F.Spratt@Dartmouth.Edu (e-mail) > > Data is not information; > Information is not knowledge; > Knowledge is not understanding; > Understanding is not wisdom. > > - Cliff Stoll and Gary Schubert > > _______________________________________________________________________