proc genmod data=inf;

(where age=1.....depends if I need to specify age group or not. If I want all ages, then I just leave this statement out)

class area age;

model n=area period area*period / dist=nb link=log offset=logpop type3;

estimate "Area 1 v area 2*period" area*period 1 -1 0/exp e;

estimate "Area 2 v area 3*period" area*period 0 -1 1/exp e;

estimate "Area 1 v area 3*period" area*period 1 0 -1/exp e;

run;

Q1 - I have 12 different age groups. If I include 'age' in the class statement, I get slightly different IRRs and certainly different p-values than if I do not include 'age' in the class statement. Should I be including age in the class statement for the model? There are times where I want to look at everything together (all-ages) and other times where I need to specify age groups that I want to look at, so I don't know if 'age' is needed in the class statement for each model or not.

Q2 - I'm not totally sure how to be interpreting the output for the IRs and CIs. I understand these (e.g., how to interpret IRR of something like 1.3), but it isn't seeming to make sense when I'm looking at the output. For example, with the above code (NO age listed in class statement), I get the following:

Area 1 v 2: IRR (1.0007) 95% CI (0.9999-1.0015) p-value 0.1043

Area 2 v 3: IRR (0.9985) 95% CI (0.9977-0.9994) p-value 0.0007

Area 1 v 3: IRR (1.0022) 95% CI (1.0015-1.0028) p-value <0.0001

This doesn't quite make sense looking at the p-value that goes with the IRRs and CIs. However, when I DO include age in the class statement for the same dataset, I get:

Area 1 v 2: IRR (1.0006) 95% CI (0.9990-1.0022) p-value 0.4867

Area 2 v 3: IRR (0.9996) 95% CI (0.9979-1.0013) p-value 0.6697

Area 1 v 3: IRR (1.0009) 95% CI (0.9994-1.0025) p-value 0.2411

This seems to make more sense to me, as looking at the IRR and CI I wouldn't expect significant results. So does this mean that age should be included in the class statement? Thanks!