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  • Interpretation Competing Risk (Stcrreg) – Comparability of Covariates

    Dear Stata Users,

    I have a question regarding the interpretation and comparability of the hazard ratios in a competing risk model obtained from the stcrreg command (Fine and Gray’s Model). I have two competing exit reasons: Exit 1 and Exit 2. When I obtain hazard ratios for my covariates from Exit =1 it looks like the following:

    Code:
    stcrreg Covariate 1 Covariate 2 Covariate 3, compete(failcode = 2 )
    Cause
    EXIT 1
    Covariate 1 1.211
    [1.32]
    Covariate 2 0.0560*
    [-1.70]
    Covariate 3 0.2***
    [2.70]
    I interpret this as: “Covariate 3 lowers my probability of exit cause 1 by 80 %”
    When I obtain hazard ratios for my covariates from Exit =2 it looks this:
    Code:
    stcrreg Covariate 1 Covariate 2 Covariate 3, compete(failcode = 1 )
    Cause
    Exit 2
    Covariate 1 1.434**
    [2.31]
    Covariate 2 0.0409*
    [-1.85]
    Covariate 3 0.4***
    [2.31]

    I interpret this as: “Covariate 3 lowers my probability of exit cause 1 by 60 %”

    My question: Can I compare the results from Model 1 and Model 2 in regard to covariate 3?
    Is it right to interpret the difference (of 20 %) for covariate 3 between Exit 1 and Exit 2? So to say that “For individual with a higher covariate 3 the probability is 20 % lower experiencing exit 2 then exit 1?



    Thanks a lot and regards,

    John

  • #2
    No, it's not. First, if you were going to do something like that, the difference between the two would be 20 percentage points, not percent. But taking the difference between two hazard ratios is not meaningful at all. You might want to talk about the ratio of the hazard ratios, but not their difference.

    Further terminology: the outputs of the Fine and Gray model are known as subhazard ratios.

    Also, a (sub)hazard ratio of 0.8 does not mean that your probability of experiencing that outcome is reduced by 20 percent. It means that at any point in time, your probability of experiencing that outcome is reduced by 20 percent if you haven't already experienced it. The actual overall reduction in probability of experiencing the outcome will, in general, be different from 20%, depends on the length of follow-up, and is difficult to calculate.

    Comment


    • #3
      No, it's not. First, if you were going to do something like that, the difference between the two would be 20 percentage points, not percent. But taking the difference between two hazard ratios is not meaningful at all. You might want to talk about the ratio of the hazard ratios, but not their difference.

      Dear Clyde,

      As always: Many thanks for your detailed answer! And sorry for my sloppy wording in regard to the terminology.
      So if I get you right I have to calculate the ratio of the two sub hazard ratios? I searched a while but found not a satisfy answer and this might be an utterly stupid question but: How do I calculate the ratio of two sub hazard ratios for a 95 % confidence interval?
      And (2) is there a way to let STATA do this automatically?

      Thanks a lot and regards,

      John

      Comment


      • #4
        Well, normally this situation arises in the context of a comparison of the subhazard ratios for a predictor in two complementary subsets of the population. I have never seen anybody do this for two different outcomes of a competing risks model. You might try saving the estimates after both runs and then feeding them to -suest- and using -lincom- to estimate the difference of the coefficients of covariate 3 across the two models. I do not know for sure if -suest- supports -stcrreg-, but I think it does. Then exponentiating the difference of the coefficients and its 95% CI will give you an estimate and 95% CI for the ratio of subhazard ratios.

        If -suest- does not work with -stcrreg- outputs, I don't know what else to suggest.

        Comment


        • #5
          Dear Cylde,

          thanks a lot for your answer ! I will try my best to use suest and lincom to solve this matter. I will come back if I have a solution for the sake of others.

          Regards,

          John

          Comment

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