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  • Estimating time-dependent AUC after competing risk regression and either continuous or categorical data

    Dear Stata experts,

    I have a dataset with typical survival type data, and I am interested in determining time-dependent AUC's after competing risk regressions. The built in Stata function stcrreg does not appear to have a post-estimation AUC (ROC) function that I can identify (time-dependent, nor otherwise). There is an excellent 3rd party package (strocccurve) that can compute time-dependent AUC's, however, this package does not account for competing risk, and cannot estimate time-dependent AUC for variables that are categorical.

    I am attaching the following dataset below.
    the dataset contains the following:

    Time Variable: timevar
    Fail Variable: failvar: 0=no failure, 1=failure, 2= competing risk censor
    Categorical Biomarker indicator variable: Categorical_var
    Continuous Biomarker variable: Continuous_var

    Is it possible to perform an AUC calculation after a competing risk survival regression on the categorical variable and continuous variables (separately) at a specified time point, like timevar==2.5?

    Any help is greatly appreciated


    Code:
    stset timevar, failure(failvar==1)


    Code:
    * Example generated by -dataex-. For more info, type help dataex
    clear
    input float(Categorical_var Continuous_var failvar timevar)
    0    .8280001 0  1.9575633
    1       1.599 0   4.958248
    1       2.121 0  2.0698152
    2       2.673 0  1.4592744
    1        2.34 0  4.0985627
    1       1.503 0   2.510609
    1       1.494 0   3.759069
    1       2.349 0   5.234771
    1       2.397 0    6.19028
    2        .429 1 -.16427104
    2       2.112 1   6.313484
    0   .14400002 0  3.9890485
    1       2.853 0   5.021218
    1       1.836 2  .56399727
    1       1.437 0  .18069816
    3       4.308 0   3.978097
    0        .999 0   7.394935
    0   .37199995 0  1.6180698
    0       1.455 2   .5968515
    2       2.967 0   2.017796
    1         .99 0   9.445585
    0       1.104 0   4.405202
    1       1.284 0  4.0958247
    0        .714 0   .9007529
    2       1.104 0  1.2073922
    1       1.161 2  3.3483915
    1       1.494 2   1.776865
    0       1.341 0  4.1587954
    0   .26699996 0  2.3408625
    0         .78 0   4.657084
    1       1.161 0   2.702259
    1   .03900003 0   5.221081
    2       3.897 1   .7474332
    1        1.56 0   2.956879
    1       2.349 0   6.510609
    1       1.095 0   5.374401
    1   1.0469999 0   5.295003
    1       2.169 0   1.946612
    2       2.073 0   .9801506
    2        2.52 0   5.245722
    2       1.713 0   6.617385
    1       2.787 0  1.2539357
    1       2.616 0   2.494182
    1        .876 0   5.097878
    1        .762 0   1.377139
    1       2.283 0    4.25462
    2        1.77 0   5.612594
    1       1.494 0  1.9958932
    2       3.756 1  .05749487
    0        .885 0   3.671458
    0         .78 0   5.804244
    0       1.056 0  1.6208076
    0  -.53099996 0   .7638603
    0          .6 0    1.28679
    3        2.91 0   1.522245
    0 -.018000055 0   7.895962
    0        .315 0   5.689254
    1       1.608 0  2.0123203
    2         1.2 0   7.904175
    1        1.77 0   .3066393
    1       1.665 0   2.740589
    2       2.967 0  1.4592744
    2       2.682 1  .09856263
    0        .609 0    6.57358
    0    .8939999 0  1.0431211
    0        .837 0   .6078029
    1        .486 2          .
    1       1.836 0  3.3812456
    1       1.941 0          .
    1   .25800005 0   3.947981
    1       1.446 0  3.3648186
    0       1.104 0   .6926762
    1       2.796 0 .008213553
    0       -.588 0   5.174538
    1  -.18900003 0  4.5667353
    0        .714 0  1.2073922
    1       1.494 0  1.3826146
    0        .933 0    3.12115
    0        .837 0   2.469541
    1       1.722 0   2.255989
    1        .705 0   6.321697
    1   .54300004 0  4.1971254
    0        .885 0   .7063655
    2   4.0230002 0  1.1608487
    3       2.055 0   1.560575
    0   .09599997 0   6.811773
    0    .5519999 0   3.455168
    1       1.551 0  1.8261465
    2       2.274 0  .17522246
    0        .723 2   3.285421
    1       2.454 0   1.779603
    1       2.007 0  3.4058864
    0       1.389 0   1.393566
    0       .4518 0  10.182067
    0       -.987 0   7.036276
    1        .933 0   3.258042
    0        .999 0    7.70705
    0        .714 0   9.804244
    2       2.559 2   9.281314
    0        .942 0   1.544148
    end
    label values Categorical_var Categorical_var
    label def Categorical_var 0 "Best", modify
    label def Categorical_var 1 "Middle", modify
    label def Categorical_var 2 "High", modify
    label def Categorical_var 3 "Worst", modify
    label values failvar failvar
    label def failvar  0 "no fail", modify
    label def failvar  1 "metastasis", modify
    label def failvar  2 "Non Cancer Death", modify
    Thanks, JT

  • #2
    Any takers?

    Comment

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