I am using commands stset and then stpm2 in Stata 16 to estimate a time-to-event or survival model.
My intension is to use these commands with weights that I previously estimated in R. My problem is that my results are exactly the same before and after applying the weights. This is not the case when I run the same methods on R.
I need to use Stata because there are other methods that I would like to apply to the weighted dataset which are only available in Stata, for example stpm2cr. More important, I would like to know if I am doing something wrong in the application of weights.

Code:
*without weights

stset time, failure(status==1)
stpm2 treatment X1 X2 X3 X4, scale(hazard) df(3)
 
*with weights

stset time [pw=weights], failure(status==1)
stpm2 treatment X1 X2 X3 X4, scale(hazard) df(3)
My data:
Code:
* Example generated by -dataex-. To install: ssc install dataex
clear
input byte(time status) double weights float(treatment X1 X2 X3 X4)
 3 0 1.4604129 15        10  2605.559         8        12
11 0 23.083236 15        10        20         8  3278.887
 1 1 .48926576 15 1503.3397        20         8        12
 2 1 .46333202 15  2386.374        20         8        12
17 1  .6506184 15        10        20         8        12
 1 1 2.1885115 15   382.853        20         8        12
 6 0 .66756524 15        10        20         8        12
 5 1 1.2288773 15 145.71672        20 156.42982        12
 3 1 .65057605 15        10        20         8        12
12 0 .84084604 15        10        20         8        12
 2 1 .95130992 15 1070.0483        20         8        12
 2 1 .91851318 15        10        20         8        12
 2 0 .65573917 15        10        20         8        12
 1 1 .71186415 15        10        20         8        12
 2 1 3.2528202 15        10        20         8        12
 3 1 .92747711 15  587.6436        20         8        12
 2 0 .68974901 15        10        20         8        12
 3 1 .65163102 15 1200.7521        20         8        12
24 0 .67443723 15        10        20  12.05285        12
 1 1 1.2309379 15  1207.485        20         8        12
26 0 1.8565074 15        10        20         8        12
 3 1 .96479149 15        10        20         8        12
 2 0 .70658756 15        10        20         8        12
 5 1 .88191331 15 192.86145        20         8        12
10 1 .88146971 15        10  313.3779         8        12
14 0 .83921143 15  314.0365        20         8        12
 2 1 .83636617 15        10        20         8        12
 2 0 .04888261 15        10 3273.6006         8        12
 6 1 .65229927 15        10        20         8        12
 6 1 .42746456 15 1183.8452  1655.827         8        12
 6 0 .65086466 15        10        20         8        12
 1 1 5.1154777 15    2334.6        20         8        12
 9 1 1.1140292 15        10        20         8        12
 1 1 .13345336 15 2341.2798        20         8        12
 3 0 .62912737 15        10  2024.962         8        12
 1 1 1.6312554 15        10        20         8        12
 9 1 .78533731 15        10        20         8        12
30 0 1.0038068 15        10        20         8        12
 7 0 .70452734 15        10        20         8        12
16 0 .92091238 15        10  611.4637         8        12
 4 0 .93993108 15        10   76.1757         8        12
21 1 .65929165 15        10        20         8        12
 1 0 1.2487904 15  413.9762        20         8        12
 2 1 .16289446 15  2457.991        20         8        12
 6 1 .13342259 15 3343.1545        20         8        12
10 1 .87474915 15        10        20         8        12
 4 1 1.1920197 15 173.78465        20         8        12
30 0 .99722067 15        10        20         8        12
 2 0 .66238793 15        10        20         8        12
13 0 .88021289 15        10  453.8784         8        12
26 0 .85527902 15        10        20         8        12
 2 1 1.4221644 15        10  465.9346         8        12
10 1 .65338268 15        10        20         8        12
 1 0 .80789489 15        10        20         8        12
 2 0 1.1671576 15        10        20         8        12
 2 1 1.1651706 15 605.37604        20         8        12
15 1 .75858948 15        10 175.63806         8        12
15 1 .82070603 15        10        20         8        12
29 1  .7656754 15        10 145.83495         8        12
 3 1 .63747885 15  1489.353 1380.4884         8        12
29 0 .65339315 15        10        20         8        12
 1 1 .25808769 15 2361.5754        20         8        12
 1 1 .19123299 15 2461.6355        20         8        12
19 1 1.3935881 15        10        20         8        12
 2 1 1.6890985 15  407.8892        20         8        12
 7 1 1.5301718 15        10        20 256.97437        12
 3 1 1.4760532 15        10 187.54504         8        12
19 0   4.02731 15 1997.6754        20         8        12
 1 0 .90140108 15        10        20         8        12
 7 0 .93141544 15        10        20         8        12
 1 1  1.404244 15  88.35081        20         8 402.98465
 1 0 6.6182105 15  317.3073        20         8        12
15 1 .86335156 15        10        20  187.9944        12
 9 1 .66942946 15        10        20         8  39.63388
 1 0  .6591931 15        10        20         8        12
 2 1 .10345764 15  2500.829        20         8        12
 1 0 .28990141 15 2411.8962        20         8        12
14 0 1.0079619 15        10        20 503.33975        12
 6 0    .75937 15        10 1052.7992         8        12
 4 1 .65606119 15        10        20         8        12
15 0 .65064123 15        10        20         8        12
 4 0 .65178225 15        10  1287.235         8        12
 5 0 .48942932 15        10 1519.4556         8        12
 5 1 .72200763 15        10        20         8        12
23 0 1.3289784 15        10        20 155.78465        12
14 0 2.7602864 15        10        20         8        12
 5 0 .71546041 15 1094.6628        20         8        12
 3 0 .66982245 15        10        20         8        12
22 0 .77654105 15        10        20  248.5658        12
12 0 .97220879 15        10        20         8        12
 1 1 .39323613 15 1608.7666        20         8        12
 6 0 .71816694 15        10        20         8        12
 4 1 .44544529 15 1630.9617        20         8        12
10 1 1.3503812 15        10        20 17.642162        12
 7 0 .95570709 15        10        20         8        12
 1 1 6.0525292 15        10        20         8        12
 6 1 3.4503232 15        10        20         8        12
30 0 .66612439 15        10        20         8        12
 8 0 .69383394 15        10        20         8        12
 4 1 .70326604 15        10        20         8        12
end