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


From   Steve Samuels <sjsamuels@gmail.com>
To   statalist@hsphsun2.harvard.edu
Subject   Re: st: FW: stcrreg: when the proportional hazards assumption fails
Date   Mon, 25 Oct 2010 17:53:21 -0400

You might also be interested in a thread about -stcrreg- and
stratification started at
http://www.stata.com/statalist/archive/2009-10/msg00614.html.

In addition, you might consider the competing risks analysis on pp.
209 -211  of the Stata 11 Survival Manual, which uses -stcox- and
hence, can apparently accommodate strata.  (The example is continued
on page 226.).

Steve

Steven J. Samuels
sjsamuels@gmail.com
18 Cantine's Island
Saugerties NY 12477
USA
Voice: 845-246-0774
Fax:    206-202-4783



On Fri, Oct 22, 2010 at 8:43 AM, Steve Samuels <sjsamuels@gmail.com> wrote:
> --
>
> Zoe:
>
> So, num_cancers has time-varying constants  for categories 2 and 3 vs
> 0, but not for  1 vs 0.  I had something different in mind.
>
>
> 1. First run stcreg with the tvc() specified for num_cancers 2 vs 0, 3
> vs 0, and texp() at whatever you specified, (You will still want
> i.num_cancers in the other part of the model).  Call it model A.
>
> 2. Then generate the predicted CIFs holding num_cancers at values 0,
> 1, 2, 3  and other covariates at single specified values.
>
> 3. Next run stcreg four times: if num_cancers = 0, 1, 2, 3. It's still
> possible that for the main-effects, num_cancers = 0 and num_cancers= 1
> are different, so you want  different runs for num_cancers= 0 and
> num_cancers=1
>
> 4. Generate the  predicted  CIF plots for model and compare to
> corresponding plot from model A.  -stcurve- will generate results, so
> that you can plot each pair together.  And, yes, just eyeball the
> results.
>
> I'll be away from my computer for the next couple of days, so won't be
> able to respond further for awhile.
>
> Good luck.
>
> Steve
>
> Steven J. Samuels
> sjsamuels@gmail.com
>
>
> On Fri, Oct 22, 2010 at 3:57 AM, Zoe Hyde <zhyde@meddent.uwa.edu.au> wrote:
>> 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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>> *   http://www.ats.ucla.edu/stat/stata/
>>
>

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