I am trying to run what should be a straightforward Poisson regression to obtain adjusted mortality rates at county-level. I must first clarify that my issue is not about choosing between a marginal (GEE) vs. subject-specific model. My data consists of the following variables: y=death counts, Ni=population at risk, sex=(M/F), Agegrp=3 age groups, and other covariates: x1=% of educated (at county level), x2=% of employed (at county level). My aim is to obtain Mortality rates/100,000 at county level based on the model consisting of the explanatory variables as shown below: I am aiming to fit the following Poisson regression model (with an offset log(Ni) : log(lambda_i)= bo + b1*Agegrp + b2*Sex + b3*X1 + b4*X2 + ui where ui=county random effects. I tried following the example from SAS help using random effects model fitted using PROC GLIMMIX (v9.4). This approach seems to do the trick, but the problem is the rates I am getting are not at county-level but rather at the level of the explanatory variables. Is there a way I could obtain these at county level? At the end I want to rank these counties by the adjusted mortality rates from the Poisson regression. Fitting a marginal mode (GEE) did not help either. I would welcome any suggestions. The code is as below: PROC GLIMMIX DATA=mydata; CLASS county; MODEL deaths = agegrp sex X1 X2 / DIST=poisson OFFSET=log_Ni S DDFM=Satterth; RANDOM county; My_Rate= 100000*exp(_zgamma_ + _xbeta_); ID county deaths pop My_Rate; OUTPUT OUT=got_you; RUN;
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