Poisson vs. Logistic regression

data melanoma;
      input age $ region $ cases total;
      ltotal=log(total);
      datalines;
   35-44 south 75  220407
   45-54 south 68  198119
   55-64 south 63  134084
   65-74 south 45   70708
   75+   south 27   34233
   <35   south 64 1074246
   35-44 north 76  564535
   45-54 north 98  592983
   55-64 north 104 450740
   65-74 north 63  270908
   75+   north 80  161850
   <35   north 61 2880262
   ;
proc genmod data=melanoma order=data;
  class age region;
  model cases = age region / dist=poisson link=log offset=ltotal;
run;

proc sql;
  create table melanoma2 as
  select age, region, 1 as resp, cases as count  from melanoma union
  select age, region, 0 as resp, total-cases as count  from melanoma 
;quit;

proc genmod data=melanoma2;
  weight count;
  class age region;
  model resp=age region / dist=binomial link=logit;
run;

proc mixed

data bond;
  input ingot metal $ pres @@;
  datalines;
1   n   67.0  1   i   71.9  1   c   72.2
2   n   67.5  2   i   68.8  2   c   66.4
3   n   76.0  3   i   82.6  3   c   74.5
4   n   72.7  4   i   78.1  4   c   67.3
5   n   73.1  5   i   74.2  5   c   73.2
6   n   65.8  6   i   70.8  6   c   68.7
7   n   75.6  7   i   84.9  7   c   69.0
;

proc mixed data=bond method=reml;
   class ingot metal;
   model pres=metal;
   random ingot;
   lsmeans metal / diff=control('n') adjust=dunnett;
run;

/* by simulation */
lsmeans metal / diff=control("n") cl
                adjust=simulate(report seed=4943838 cvadjust);