RE: st: FW: stcrreg: when the proportional hazards assumption fails

["Zoe Hyde" <zhyde@meddent.uwa.edu.au>]

Thanks, Steve.

Sorry, there are four levels to the ordinal variable - I was 
forgetting the reference category.

Regarding your suggestion, do you mean something like this:


stset d_event, failure(compete==2) origin(d_dob) entry(d_clinicdate)
id(id) scale(365.25)

stcrreg i.lh_quintile i.numcancers prevcvd age whr hyp dyslipid i.smoker
diabetes if numcancers == 0 | numcancers == 1, compete(compete==1)
stcurve, cif at1(lh_quintile=0) at2(lh_quintile=1) at3(lh_quintile=2)
at4(lh_quintile=3) at5(lh_quintile=4)

stcrreg i.lh_quintile i.numcancers prevcvd age whr hyp dyslipid i.smoker
diabetes if numcancers == 0 | numcancers == 2, compete(compete==1)
stcurve, cif at1(lh_quintile=0) at2(lh_quintile=1) at3(lh_quintile=2)
at4(lh_quintile=3) at5(lh_quintile=4)

stcrreg i.lh_quintile i.numcancers prevcvd age whr hyp dyslipid i.smoker
diabetes if numcancers == 0 | numcancers == 3, compete(compete==1)
stcurve, cif at1(lh_quintile=0) at2(lh_quintile=1) at3(lh_quintile=2)
at4(lh_quintile=3) at5(lh_quintile=4)


...and then just eyeballing the results?  The curves look
pretty much identical.


Zoe.


>On Thu, Oct 21, 2010 at 04:13 PM, Steve Samuels <sjsamuels@gmail.com>
wrote:
>Zoe-
>
>Ah, I see what you mean. The tvc() coefficients provide evidence of
>non-proportionality, but might not provide the correct model. With
>regular Cox, we'd stratify by categories of the offending variable, as
>you say, but that's not available here. -stcompadj- (from SSC) also
>does not provide a stratified analysis.
>
>One possibility: run the model in the two (three?) subgroups of your
>ordinal variable that violate proportionality. Compare the separate
>cumulative incidence curves to that predicted by -stcrreg- or
>-stcompadj-. Perhaps they are close, and you have a good model after
>all.
>
>Otherwise, store the estimates of coefficients of the variables common
>to all the models and compute weighted averages, weighting by the
>inverses of the estimated variances.  I know this is easier said than
>done!
>
>Steve
>
>Steven J. Samuels
>sjsamuels@gmail.com
>18 Cantine's Island
>Saugerties NY 12477
>USA
>Voice: 845-246-0774
>Fax:    206-202-4783
>
>
>
>On Thu, Oct 21, 2010 at 10:06 AM, Steve Samuels <sjsamuels@gmail.com>
wrote:
>> Zoe:
>>
>> I don't see that you have a problem. You seem to have a fairly
>> complete model if you include the ordinal variable with the tvc() and
>> texp() commands, perhaps omitting the non-significant indicator. As
>> the Stata 11 Manual states on p 214, it is the coefficients which are
>> time varying.
>>
>> One issue: a three-level variable would have only two indicators, not
>> three. Showing your code and results, as the FAQ request, would
really
>> help avoid this kind of misunderstanding.
>>
>> Steve
>>
>> Steven J. Samuels
>> sjsamuels@gmail.com
>> 18 Cantine's Island
>> Saugerties NY 12477
>> USA
>> Voice: 845-246-0774
>> Fax:    206-202-4783
>>
>> On Thu, Oct 21, 2010 at 4:45 AM, Zoe Hyde <zhyde@meddent.uwa.edu.au>
wrote:
>>> Hello All,
>>>
>>> I am wondering what options are available when the proportional
hazards assumption 
>>> doesn't hold in a competing-risks regression.  The assumption holds
for my main 
>>> independent variable of interest, but not for another (ordinal)
variable that I'd 
>>> like to adjust for; fitting it as a time-varying covariate gives a
significant 
>>> result for 2 of its 3 levels.
>>>
>>> I could get around this by stratifying by this variable in a
standard Cox model, 
>>> but this doesn't seem to be supported (yet) by stcrreg.
>>>
>>> Are there any alternatives?
>>>
>>>
>>> Regards,
>>>
>>> Zoe.
>>>
>>>
>>> Western Australian Centre for Health and Ageing (M570)
>>> University of Western Australia
>>> 35 Stirling Highway, Crawley 6009
>>> Western Australia
>>>
>>> Courier address:
>>> Level 6, Ainslie House, Royal Perth Hospital
>>> 48 Murray Street, Perth 6000

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