Stata MP 14.2, OSX 10.15.4

I am looking for an approach, be it by simulation or other, that will help me address a question.

As background, a recent systematic review has shown that the mortality rate in large randomised clinical trials (RCTs) of dialysis patients (analysing 79,104 participating patients) was only half of that in the population-based registry data (i.e. the source population of "real world patients" from which they were drawn) to which they were compared. These mortality rates were 8.9 versus 18.6 deaths per 100 patient years, respectively.

I have a population-based -stset- dataset from my own country, with a mortality rate of 15.9 per 100 patient years.

I'm looking determine the rough proportion of patients in my own country for whom the RCT findings would be generalisable. To do this, I was looking to see in a hypothetic manner what proportion of patients in my dataset have the same mortality risk profile as that of the RCT participants in those trials - i.e. they have a mortality rate that is ~50% of the group average, which in my case amounts to an average mortality rate of around 7.6 deaths per 100 patient years).

I was hoping there was a smart way of doing it with a sampling method that produces a subsample that is a reasonable reflection of reality. Obviously, knocking out higher mortality rate patients until the overall - strate - comes down to 7.6 produces weird looking survival curves.

Any advice?

Thanks

MM

HTML Code:
. strate

         failure _d:  dead == 1
   analysis time _t:  (end_recorddate-origin)/365.25
             origin:  time rxdate
  exit on or before:  time exitdate
                 id:  mrm_patient

Estimated rates and lower/upper bounds of 95% confidence intervals
(27944 records included in the analysis)

  +----------------------------------------------+
  |    D         Y      Rate     Lower     Upper |
  |----------------------------------------------|
  | 5211   3.3e+04   0.15928   0.15501   0.16366 |
  +----------------------------------------------+
HTML Code:
. stdes

         failure _d:  dead == 1
   analysis time _t:  (end_recorddate-origin)/365.25
             origin:  time rxdate
  exit on or before:  time exitdate
                 id:  mrm_patient

                                   |-------------- per subject --------------|
Category                   total        mean         min     median        max
------------------------------------------------------------------------------
no. of subjects             9387   
no. of records             27944    2.976883           1          3         18

(first) entry time                  .0010083           0          0   2.023272
(final) exit time                   3.488286    .0027379   2.740589   19.88501

subjects with gap             30   
time on gap if gap     18.354552    .6118184    .0027379   .1943874   5.585216
time at risk           32716.723    3.485323    .0027379   2.737851   19.88501

failures                    5211    .5551294           0          1          1
------------------------------------------------------------------------------