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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 >> >> * >> * For searches and help try: >> * http://www.stata.com/help.cgi?search >> * http://www.stata.com/support/statalist/faq >> * http://www.ats.ucla.edu/stat/stata/ >> > * * For searches and help try: * http://www.stata.com/help.cgi?search * http://www.stata.com/support/statalist/faq * http://www.ats.ucla.edu/stat/stata/

**References**:**RE: st: FW: stcrreg: when the proportional hazards assumption fails***From:*"Zoe Hyde" <zhyde@meddent.uwa.edu.au>

**Re: st: FW: stcrreg: when the proportional hazards assumption fails***From:*Steve Samuels <sjsamuels@gmail.com>

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