Confidence intervals for cumulative incidence function after stccreg.

[Sumedha Gupta]

Dear All,
I want to estimate confidence intervals on the estimated cumulative incidence functions generated by stcurve after running a stccreg regression. The non-parametric stcompet option is not what I am looking for. Here is my dataex:

* Example generated by -dataex-. For more info, type help dataex
clear
input float(COVID deaths_rate cum_num_vacpct tavg lnprcp grpatidtreat monthlydate) str24 state byte(Female age80plus) float(CumMonthsSAH lnnursing_visits) byte(_st _d _t _t0) float(stop start)
0 0 0 26.89387 4.625463 4825785 714 "AL" 1 0 0 11.36908 1 0 1 0 1 0
0 0 0 26.89387 4.625463 4296350 714 "AL" 1 0 0 11.36908 1 0 1 0 1 0
0 0 0 26.89387 4.625463 2609232 714 "AL" 0 1 0 11.36908 1 0 1 0 1 0
0 0 0 26.89387 4.625463 3845592 714 "AL" 1 0 0 11.36908 1 0 1 0 1 0
0 0 0 26.89387 4.625463 2931233 714 "AL" 1 1 0 11.36908 1 0 1 0 1 0
0 0 0 26.89387 4.625463 4855171 714 "AL" 1 0 0 11.36908 1 0 1 0 1 0
0 0 0 26.89387 4.625463 3097706 714 "AL" 1 0 0 11.36908 1 0 1 0 1 0
0 0 0 26.89387 4.625463 3141305 714 "AL" 1 1 0 11.36908 1 0 1 0 1 0
0 0 0 26.89387 4.625463 2406805 714 "AL" 1 0 0 11.36908 1 0 1 0 1 0
0 0 0 26.89387 4.625463 2627631 714 "AL" 1 0 0 11.36908 1 0 1 0 1 0
0 0 0 26.89387 4.625463 2672092 714 "AL" 1 0 0 11.36908 1 0 1 0 1 0
0 0 0 26.89387 4.625463 4786947 714 "AL" 1 0 0 11.36908 1 0 1 0 1 0
0 0 0 26.89387 4.625463 2959691 714 "AL" 0 0 0 11.36908 1 0 1 0 1 0
0 0 0 26.89387 4.625463 3792777 714 "AL" 1 0 0 11.36908 1 0 1 0 1 0
0 0 0 26.89387 4.625463 2937359 714 "AL" 1 0 0 11.36908 1 0 1 0 1 0
0 0 0 26.89387 4.625463 2738875 714 "AL" 1 0 0 11.36908 1 0 1 0 1 0
0 0 0 26.89387 4.625463 2700808 714 "AL" 0 1 0 11.36908 1 0 1 0 1 0
0 0 0 26.89387 4.625463 3011217 714 "AL" 1 0 0 11.36908 1 0 1 0 1 0
0 0 0 26.89387 4.625463 3351107 714 "AL" 1 0 0 11.36908 1 0 1 0 1 0
0 0 0 26.89387 4.625463 3001171 714 "AL" 0 0 0 11.36908 1 0 1 0 1 0
0 0 0 26.89387 4.625463 4434278 714 "AL" 1 0 0 11.36908 1 0 1 0 1 0
0 0 0 26.89387 4.625463 3102923 714 "AL" 1 1 0 11.36908 1 0 1 0 1 0
0 0 0 26.89387 4.625463 3342011 714 "AL" 1 0 0 11.36908 1 0 1 0 1 0
0 0 0 26.89387 4.625463 3513849 714 "AL" 1 0 0 11.36908 1 0 1 0 1 0
0 0 0 26.89387 4.625463 3135530 714 "AL" 1 1 0 11.36908 1 0 1 0 1 0
0 0 0 26.89387 4.625463 2713157 714 "AL" 1 1 0 11.36908 1 0 1 0 1 0
0 0 0 26.89387 4.625463 4257263 714 "AL" 0 0 0 11.36908 1 0 1 0 1 0
0 0 0 26.89387 4.625463 2599746 714 "AL" 1 1 0 11.36908 1 0 1 0 1 0
0 0 0 26.89387 4.625463 2524957 714 "AL" 1 1 0 11.36908 1 0 1 0 1 0
0 0 0 26.89387 4.625463 4436744 714 "AL" 1 0 0 11.36908 1 0 1 0 1 0
0 0 0 26.89387 4.625463 3060035 714 "AL" 0 0 0 11.36908 1 0 1 0 1 0
0 0 0 26.89387 4.625463 4202188 714 "AL" 0 0 0 11.36908 1 0 1 0 1 0
0 0 0 26.89387 4.625463 3070228 714 "AL" 1 0 0 11.36908 1 0 1 0 1 0
0 0 0 26.89387 4.625463 2666122 714 "AL" 1 0 0 11.36908 1 0 1 0 1 0
0 0 0 26.89387 4.625463 3801665 714 "AL" 0 0 0 11.36908 1 0 1 0 1 0
0 0 0 26.89387 4.625463 4313203 714 "AL" 1 0 0 11.36908 1 0 1 0 1 0
0 0 0 26.89387 4.625463 2457749 714 "AL" 1 1 0 11.36908 1 0 1 0 1 0
0 0 0 26.89387 4.625463 3808970 714 "AL" 1 0 0 11.36908 1 0 1 0 1 0
0 0 0 26.89387 4.625463 4894145 714 "AL" 0 0 0 11.36908 1 0 1 0 1 0
0 0 0 26.89387 4.625463 4622302 714 "AL" 1 0 0 11.36908 1 0 1 0 1 0
0 0 0 26.89387 4.625463 3225875 714 "AL" 0 0 0 11.36908 1 0 1 0 1 0
0 0 0 26.89387 4.625463 4419240 714 "AL" 1 1 0 11.36908 1 0 1 0 1 0
0 0 0 26.89387 4.625463 4561300 714 "AL" 0 0 0 11.36908 1 0 1 0 1 0
0 0 0 26.89387 4.625463 2716008 714 "AL" 1 0 0 11.36908 1 0 1 0 1 0
0 0 0 26.89387 4.625463 4904120 714 "AL" 1 0 0 11.36908 1 0 1 0 1 0
0 0 0 26.89387 4.625463 3161244 714 "AL" 1 0 0 11.36908 1 0 1 0 1 0
0 0 0 26.89387 4.625463 2359942 714 "AL" 1 1 0 11.36908 1 0 1 0 1 0
0 0 0 26.89387 4.625463 3441568 714 "AL" 1 0 0 11.36908 1 0 1 0 1 0
0 0 0 26.89387 4.625463 3230359 714 "AL" 1 0 0 11.36908 1 0 1 0 1 0
0 0 0 26.89387 4.625463 4735391 714 "AL" 1 0 0 11.36908 1 0 1 0 1 0
0 0 0 26.89387 4.625463 2929492 714 "AL" 1 0 0 11.36908 1 0 1 0 1 0
0 0 0 26.89387 4.625463 4512910 714 "AL" 1 0 0 11.36908 1 0 1 0 1 0
0 0 0 26.89387 4.625463 4136783 714 "AL" 1 0 0 11.36908 1 0 1 0 1 0
0 0 0 26.89387 4.625463 5005909 714 "AL" 1 0 0 11.36908 1 0 1 0 1 0
0 0 0 26.89387 4.625463 4607796 714 "AL" 1 1 0 11.36908 1 0 1 0 1 0
0 0 0 26.89387 4.625463 2575344 714 "AL" 1 1 0 11.36908 1 0 1 0 1 0
0 0 0 26.89387 4.625463 3238279 714 "AL" 0 0 0 11.36908 1 0 1 0 1 0
0 0 0 26.89387 4.625463 2705306 714 "AL" 0 0 0 11.36908 1 0 1 0 1 0
0 0 0 26.89387 4.625463 3186045 714 "AL" 1 0 0 11.36908 1 0 1 0 1 0
0 0 0 26.89387 4.625463 4641303 714 "AL" 1 0 0 11.36908 1 0 1 0 1 0
0 0 0 26.89387 4.625463 4745426 714 "AL" 0 0 0 11.36908 1 0 1 0 1 0
0 0 0 26.89387 4.625463 4737993 714 "AL" 1 0 0 11.36908 1 0 1 0 1 0
0 0 0 26.89387 4.625463 4572224 714 "AL" 0 1 0 11.36908 1 0 1 0 1 0
0 0 0 26.89387 4.625463 3102996 714 "AL" 1 1 0 11.36908 1 0 1 0 1 0
0 0 0 26.89387 4.625463 3241614 714 "AL" 1 0 0 11.36908 1 0 1 0 1 0
0 0 0 26.89387 4.625463 3402434 714 "AL" 0 0 0 11.36908 1 0 1 0 1 0
0 0 0 26.89387 4.625463 2939381 714 "AL" 1 0 0 11.36908 1 0 1 0 1 0
0 0 0 26.89387 4.625463 3844655 714 "AL" 1 0 0 11.36908 1 0 1 0 1 0
0 0 0 26.89387 4.625463 4733580 714 "AL" 0 1 0 11.36908 1 0 1 0 1 0
0 0 0 26.89387 4.625463 2461308 714 "AL" 1 1 0 11.36908 1 0 1 0 1 0
0 0 0 26.89387 4.625463 4509034 714 "AL" 1 1 0 11.36908 1 0 1 0 1 0
0 0 0 26.89387 4.625463 2598118 714 "AL" 1 1 0 11.36908 1 0 1 0 1 0
0 0 0 26.89387 4.625463 2473039 714 "AL" 1 1 0 11.36908 1 0 1 0 1 0
0 0 0 26.89387 4.625463 2616427 714 "AL" 1 0 0 11.36908 1 0 1 0 1 0
0 0 0 26.89387 4.625463 4124404 714 "AL" 1 0 0 11.36908 1 0 1 0 1 0
0 0 0 26.89387 4.625463 4315043 714 "AL" 1 0 0 11.36908 1 0 1 0 1 0
0 0 0 26.89387 4.625463 4555432 714 "AL" 1 0 0 11.36908 1 0 1 0 1 0
0 0 0 26.89387 4.625463 4907557 714 "AL" 0 0 0 11.36908 1 0 1 0 1 0
0 0 0 26.89387 4.625463 3601099 714 "AL" 0 0 0 11.36908 1 0 1 0 1 0
0 0 0 26.89387 4.625463 4841560 714 "AL" 0 0 0 11.36908 1 0 1 0 1 0
0 0 0 26.89387 4.625463 2596517 714 "AL" 1 0 0 11.36908 1 0 1 0 1 0
0 0 0 26.89387 4.625463 2742700 714 "AL" 1 0 0 11.36908 1 0 1 0 1 0
0 0 0 26.89387 4.625463 4082672 714 "AL" 0 0 0 11.36908 1 0 1 0 1 0
0 0 0 26.89387 4.625463 4170902 714 "AL" 1 0 0 11.36908 1 0 1 0 1 0
0 0 0 26.89387 4.625463 4601678 714 "AL" 1 0 0 11.36908 1 0 1 0 1 0
0 0 0 26.89387 4.625463 4438548 714 "AL" 1 0 0 11.36908 1 0 1 0 1 0
0 0 0 26.89387 4.625463 4702746 714 "AL" 0 0 0 11.36908 1 0 1 0 1 0
0 0 0 26.89387 4.625463 3592304 714 "AL" 1 0 0 11.36908 1 0 1 0 1 0
0 0 0 26.89387 4.625463 4587829 714 "AL" 1 0 0 11.36908 1 0 1 0 1 0
0 0 0 26.89387 4.625463 4369520 714 "AL" 0 1 0 11.36908 1 0 1 0 1 0
0 0 0 26.89387 4.625463 4796518 714 "AL" 1 0 0 11.36908 1 0 1 0 1 0
0 0 0 26.89387 4.625463 4845602 714 "AL" 0 0 0 11.36908 1 0 1 0 1 0
0 0 0 26.89387 4.625463 4247550 714 "AL" 0 0 0 11.36908 1 0 1 0 1 0
0 0 0 26.89387 4.625463 3933713 714 "AL" 0 0 0 11.36908 1 0 1 0 1 0
0 0 0 26.89387 4.625463 2422566 714 "AL" 1 1 0 11.36908 1 0 1 0 1 0
0 0 0 26.89387 4.625463 4063063 714 "AL" 0 0 0 11.36908 1 0 1 0 1 0
0 0 0 26.89387 4.625463 3687858 714 "AL" 1 0 0 11.36908 1 0 1 0 1 0
0 0 0 26.89387 4.625463 3092223 714 "AL" 1 0 0 11.36908 1 0 1 0 1 0
0 0 0 26.89387 4.625463 4830458 714 "AL" 1 0 0 11.36908 1 0 1 0 1 0
0 0 0 26.89387 4.625463 2615431 714 "AL" 1 1 0 11.36908 1 0 1 0 1 0
end
format %tm monthlydate
label values COVID COVID
label def COVID 0 "No confirmed COVID-19", modify

I thought I could bootstrap the CI's using the following code, but cif is not an option for predict. Might there be another way?

stset stop, id(grpatid) enter(start) failure(d2=1) time0(start)

. program define cif_boot_covid, rclass
  1.     version 17
  2.     syntax
  3. 
.     * Re-run the 2SRI model within each bootstrap sample
.                 reghdfe COVID deaths_rate cum_num_vacpct tavg lnprcp, absorb(grpatid monthlydate state, save) cluster(grpatid) res
> iduals(resid) 
  4.                 predict double COVID_fe, r
  5.                 stcrreg Female age80plus CumMonthsSAH lnnursing_visits  COVID COVID_fe, compete(d2=2) 
  6.                 drop resid COVID_fe
  7. * Predict CIF for COVID==0
.     predict cif0, cif at(COVID=0) 
  8.     summarize cif0
  9.     return scalar cif0_mean = r(mean)
 10. 
.     * Predict CIF for COVID==1
.     predict cif1, cif at(COVID=1) 
 11.     summarize cif1
 12.     return scalar cif1_mean = r(mean)
 13. end

. 
. bootstrap r(cif0_mean) r(cif1_mean), reps(10) seed(12345) ///
>     bca level(95) saving(cif_boot_covid_results_Black, replace): cif_boot_covid 
option cif not allowed
an error occurred when bootstrap executed cif_boot_covid
r(198);

I will be grateful for any help anyone may be able to offer.
Many thanks,
Sumedha

[Sumedha Gupta]

I tried this instead and got the following error:

. * Step 1: Define program to extract CIF at a fixed time point (e.g., t = 31)
. program define cif_boot_covid, rclass
  1.     version 17.0
  2. 
.         * linear first stage
.         reghdfe COVID deaths_rate cum_num_vacpct tavg lnprcp, absorb(grpatid monthlydate state, save) cluster(grpatid) residuals(r
> esid) 
  3.         predict double COVID_fe, r
  4. 
.     * 2nd stage stcrreg with fitted value control
.     stcrreg Female age80plus CumMonthsSAH lnnursing_visits COVID COVID_fe, compete(d2=2)
  5. 
.     * Estimate CIF at COVID=1 and COVID=0 using stcurve and save
.     tempfile cifout
  6.     stcurve, cif at(COVID=(0 1)) outfile(`cifout', replace)
  7.         drop resid COVID_fe
  8. 
.     * Load saved CIF data
.     use `cifout', clear
  9. 
.     * Choose a specific time point (e.g., t = 30)
.     preserve
 10.     keep if _t==30
 11.     gen cif_diff = ci2 - ci1
 12.     return scalar diff30 = cif_diff[1]
 13.     restore
 14. end

. 
. bootstrap r(diff30) /*save(cif_boot_covid_results_Black, replace)*/, reps(10) seed(12345): cif_boot_covid
(running cif_boot_covid on estimation sample)

warning: cif_boot_covid does not set e(sample), so no observations will be excluded from the resampling because of missing values
         or other reasons. To exclude observations, press Break, save the data, drop any observations that are to be excluded, and
         rerun bootstrap.

Bootstrap replications (10): 
type mismatch:  exp.exp:  transmorphic found where struct expected
r(3000);

end of do-file

r(3000);

Any help in running the bootstrap to generate 95%CI for the cumulative incidence functions would be very very welcome.
Gratefully,
Sumedha