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From |
"Zoe Hyde" <zhyde@meddent.uwa.edu.au> |

To |
<statalist@hsphsun2.harvard.edu> |

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

Date |
Fri, 22 Oct 2010 15:57:04 +0800 |

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/

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

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