Just to note something you might consider - if you have a count of some event recorded in cities and you have the population sizes, then typically the interest is in modeling the rate, count/size. Size would be used as an offset in the count model. That can be easily done by creating a variable as log(size) and then using that in the OFFSET= option in a log-linked Poisson model. For example,  proc genmod; model pa_days=race sex depression / d=p offset=logsize; run; ... View more

Correct. Since the model is saturated, you'll notice that that is the same as the difference in those means if you compute them using, say, PROC MEANS. But you need GENMOD to get a standard error and confidence limits. ... View more

If your goal as stated means that you want to assess the difference between males and females in the bachelors group, then you can use the LSMEANS statement to do that. In the differences table from the following, the first row makes that comparison. proc genmod data=results; class bachelors(ref='0') male(ref='0'); model years_worked= bachelors male bachelors*male / dist=gamma link=log; lsmeans bachelors*male / diff exp cl plots=none; run; ... View more

See "Mahalanobis distances" and "Outlier detection" in our list of Frequently Asked for Statistics (FASTats) at http://support.sas.com/kb/30333  ... View more

You should be able to do this more simply using the joint modeling approach available in PROC GLIMMIX. See the example titled "Joint Modeling of Binary and Count Data" in the GLIMMIX documentation. ... View more

See this note and the examples it links to. ... View more

If you want to make inferences at the population level, you could use PROC GEE to fit the population-averaged, Generalized Estimating Equations model. (You can also use GENMOD for GEE models, but PROC GEE is the newer procedure and is recommended for such models). See the binary response example in the Getting Started section of the PROC GEE documentation for syntax. If you want your inferences at the individual level, then you can stay with a mixed model approach with random effects by using PROC GLIMMIX. See the logistic regression example in the GLIMMIX documentation.  ... View more

You get a single p-value for each model term if you add the TYPE3 option in the MODEL statement. By default, there are score tests for a GEE model, or you can also add the WALD option to get Wald tests instead. ... View more

Your code shows a response variable called TIME. This does not seem to be the binary indicator of disease that you mention. If the binary disease variable is actually what your model is supposed to predict, then use PROC LOGISTIC, not PROC PHREG.    However, regardless of the models generating the p-values, adjustment of p-values for multiple testing can be done using PROC MULTTEST which offers many modern p-value adjustment methods. The Bonferroni method is extremely conservative (many would say much too conservative). Using a method such as the Holm method in MULTTEST is more powerful and controls for the family-wise error rate.  You can use MULTTEST by gathering all of the p-values for your biomarkers into a single variable in a single data set and then using MULTTEST to adjust the p-values with the adjustment method of your choice, such as Holm. This is illustrated in the example titled "Inputting Raw p-Values" in the MULTTEST documentation. ... View more

If "many of the subjects" means that some subjects do have repeated measures then you still have correlation. Of course, it is up to you if you want to ignore the correlation and assume that all measurements are independent. ... View more

The description of your study implies that your subjects are observed repeatedly over time. Neither of your analyses takes the resulting correlation among the repeated measures into account. See the last section titled "Treated and Control Groups, Binary Response" in this note. The estimate named "1 month change  diff" produced using the Margins macro, or the estimate named "adjusted exp change" using the NLMeans macro, compare the change from pre to post in the exposed vs. unexposed groups like what you want to do.  ... View more