Re: st: Proportionality check: stcrreg with an established tvc

[Adam Olszewski <adam.olszewski@gmail.com>]

Hi Nicole,
I am not aware of any literature specifically discussing this issue. I
usually test  texp(_t)  and ln(_t). The more testing, the more your
P=.05 for multiple comparisons becomes inadequate. There is a good
chance that in most settings a function of time can be found, for
which the interaction with your variable will result in a significant
P value (although I am not going to try to make a mathematical proof
of it). So all within reason, unless there is data to support a
specific strategy.
AO

On Fri, Aug 16, 2013 at 6:22 PM, Boyle, Nicole M <nboyle@fhcrc.org> wrote:
> Hi AO,
>
> Wonderful, thanks for your advice! I appreciate it.
>
> Follow-up question: For deeming the "appropriateness" of other time function interactions,
> does one also eyeball the results of different outputs in the same manner you described?
> I've been considering using texp(ln(_t)), only because this is the only way tvc was taught
> to me, but that's obviously not a good enough reason to transform a time function.
>
> Thanks,
> Nicole
> ________________________________________
> From: owner-statalist@hsphsun2.harvard.edu [owner-statalist@hsphsun2.harvard.edu] on behalf of Adam Olszewski [adam.olszewski@gmail.com]
> Sent: Friday, August 16, 2013 3:07 PM
> To: statalist@hsphsun2.harvard.edu
> Subject: Re: st: Proportionality check: stcrreg with an established tvc
>
> Hi Nicole,
> It is actually enough to inspect the results of the stcrreg alone. If
> the hypothesis that TVC coefficient for your other variables can be
> safely rejected based on the P-values, then this is all you need. You
> may, for additional sensitivity, evaluate tvc with the texp() function
> to assure that interactions with other time functions are not more
> appropriate (e.g. texp(_t^2) or texp(ln(_t)) ).
> I am not sure if there is much value of doing a "general" test of
> proportionality of all variables at the same time, you would probably
> want more to know which variable violates the PH assumption and how
> important it is for your estimands of interest to include this effect
> in the model.
> AO
>
> On Fri, Aug 16, 2013 at 5:55 PM, Boyle, Nicole M <nboyle@fhcrc.org> wrote:
>> Hello all,
>>
>> I'm fitting a model in stcrreg and am trying to check for proportional hazards via the time dependent
>> covariate method. I already have a tvc in my model, and I don't know how to properly check for
>> proportionality of the other non-time varying covariates.
>>
>> Here's the model I want to use in my study; I've included "grade" as a tvc, as grade's time-dependence is
>> relevant to our study:
>>
>>         stcrreg myel i.grft i.prop i.year age grade, compete(failure==2) tvc(grade)
>>
>>
>> Here's the model I'd like to use to check for proportionality, where all vars are now tvc:
>>
>>         stcrreg myel i.grft i.prop i.year age grade, compete(failure==2) tvc(myel i.grft i.prop i.year age grade)
>>
>> Then followed by the postestimation Wald test, which will test if any of the tvcs in the model are significant:
>>
>>         test [tvc]
>>
>>
>> My problem:
>> I don't want to leave tvc(grade) out of this test, since proportionality of the other covariates
>> may change in the presence/absence of tvc(grade). However, keeping tvc(grade) in this test may also result
>> in a significant Wald test, and therefore an artifactual proportional hazards violation if tvc(grade) is in fact
>> significant.
>>
>> Should I instead use this postestimation command...
>>
>>         test [tvc], mtest
>>
>> ...which will test for the presence any time-dependence AS WELL AS individual time-dependence (per each
>>  covariate)? Or should I leave "grade" completely out of the proportionality check model?
>>
>>
>> Thanks a million,
>> Nicole
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