Hi guys:
I have a question regarding stcox and stcrreg. i try to find how does the independent variable, bullying strategy (dummy), influence different kinds of ways border claims ended. Therefore, the competing risk model is an ideal choice. also, i am interesting in finding the probabilities of certain outcomes within some periods, which leads to the choice of stcrreg, as i want to generate cif for individual outcomes. I found, however, using stcox and stcrreg produces different results. Below is the example to illustrate this point.
there are in total of 9 kinds of outcomes:
Code:
  resolved2 |      Freq.     Percent        Cum.
------------+-----------------------------------
          0 |     20,685       99.66       99.66
          1 |         10        0.05       99.71
          4 |         17        0.08       99.79
          5 |          2        0.01       99.80
          6 |          2        0.01       99.81
          7 |         14        0.07       99.87
         11 |          1        0.00       99.88
         12 |          5        0.02       99.90
         13 |         11        0.05       99.96
         14 |          9        0.04      100.00
------------+-----------------------------------
      Total |     20,756      100.00
i set resolved2 == 7 as the event of interest
Code:
stset claimserialend, id(claimdy) fail(resolved2==7) origin(time claimserialstart)  enter(enterdate)
                     scale(30)
then i first used stcox
Code:
stcox i.bullying i.bdout_3 viol icowsal cumu10mid avgterg avgterv i.special, nohr nolog efron robust
and get these results:
Code:
 failure _d:  resolved2 == 7
   analysis time _t:  (claimserialend-origin)/30
             origin:  time claimserialstart
  enter on or after:  time enterdate
                 id:  claimdy

Cox regression -- Efron method for ties

No. of subjects      =           87             Number of obs    =      20,756
No. of failures      =           14
Time at risk         =  20705.33333
                                                Wald chi2(8)     =       33.02
Log pseudolikelihood =   -30.568174             Prob > chi2      =      0.0001

                               (Std. Err. adjusted for 87 clusters in claimdy)
------------------------------------------------------------------------------
             |               Robust
          _t |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
  1.bullying |   1.554178   .6693342     2.32   0.020     .2423068    2.866049
   1.bdout_3 |   1.212035   .7166583     1.69   0.091    -.1925897    2.616659
        viol |   .6764656   .3219261     2.10   0.036     .0455019    1.307429
     icowsal |   .2297885   .1818425     1.26   0.206    -.1266163    .5861933
   cumu10mid |  -.5565466   .1833163    -3.04   0.002    -.9158399   -.1972533
     avgterg |  -1.671215   .7281596    -2.30   0.022    -3.098381   -.2440479
     avgterv |   2.208843   1.918719     1.15   0.250    -1.551777    5.969463
   1.special |   .2368191   .7650886     0.31   0.757    -1.262727    1.736365
------------------------------------------------------------------------------
the first independent variable, bullying strategy dummy, is significant.
when i tried stcrreg, however, things are different:
Code:
 stcrreg i.bullying i.bdout_2 i.bdout_3 viol icowsal cumu10mid avgterg avgterv i.special,
              compete(resolved2 == 1 4 5 6 11 12 13 14) nohr nolog robust

         failure _d:  resolved2 == 7
   analysis time _t:  (claimserialend-origin)/30
             origin:  time claimserialstart
  enter on or after:  time enterdate
                 id:  claimdy

Competing-risks regression                       No. of obs       =     20,756
                                                 No. of subjects  =         87
Failure event   : resolved2 == 7                 No. failed       =         14
Competing events: (1)                            No. competing    =         57
                                                 No. censored     =         16

                                                 Wald chi2(9)     =    1620.55
Log pseudolikelihood = -45.009945                Prob > chi2      =     0.0000

                               (Std. Err. adjusted for 87 clusters in claimdy)
------------------------------------------------------------------------------
             |               Robust
          _t |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
  1.bullying |   1.167243   1.018178     1.15   0.252    -.8283493    3.162836
   1.bdout_2 |   -16.4309   1.002408   -16.39   0.000    -18.39559   -14.46622
   1.bdout_3 |   .3128215   .7729912     0.40   0.686    -1.202213    1.827856
        viol |   .5064316   .3091432     1.64   0.101    -.0994779    1.112341
     icowsal |   .1148364   .1666615     0.69   0.491     -.211814    .4414869
   cumu10mid |  -.4802709   .2456184    -1.96   0.051    -.9616742    .0011324
     avgterg |  -1.438623   .7947055    -1.81   0.070    -2.996217    .1189714
     avgterv |   3.667996   1.440916     2.55   0.011     .8438523    6.492141
   1.special |   .6250076   .8436583     0.74   0.459    -1.028532    2.278548
------------------------------------------------------------------------------
(1) resolved2 == 1 4 5 6 11 12 13 14
so in the second model, the bullying strategy dummy becomes insignificant. How to explain this difference?
but based on the second model, i can still generate cif for bullying strategy dummy variable:
Array
Thanks in advance!
Best
Jiong