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From |
moleps islon <moleps2@gmail.com> |

To |
statalist@hsphsun2.harvard.edu |

Subject |
Re: R: st: R: reporting time dependent covariates in cox regression |

Date |
Fri, 4 Sep 2009 12:35:20 +0200 |

thanks. From what, Maarten Buis, has taught me that the interpretation of the time-varying variable is not too hard. You multiply the "fixed" variable with the time varying variable raised to the power of time you're interested in, ie at time 5 (days,minutes,years-depending on your axis) you'll have the hazard ratio of the -rh-variable*-t-variable^5. So in other words a -t- variable <1 reduces the hazard ratio over time and a -t-var >1 increases the hazard ratio over time. Since the end-point in my study is death I'm not surprised to find that several of my covariates have -t-var <1. Intuitively most factors will diminish in importance as death is imposing. What is harder to interpret is an interaction between two time-varying covariates. I think what I'll do is to create a table and report my time-varying covariates in a separate row beneath the "fixed" effect in case of significance. Moleps On Fri, Sep 4, 2009 at 11:29 AM, Carlo Lazzaro<carlo.lazzaro@tin.it> wrote: > Dear Moleps, > in the first part of my previous reply I misunderstood that, from the very start of your message, you focused on tvc and not on proportional hazard condition only. > > <Cleves is a good book, [...] they dont either give any clues as to how results with -tvc-should be presented.> > > I agree with you that more examples can help readers. Hopefully this might be improved in the next edition of this helpful textbook. > However, at pag 169 of the revised edition (par 10.5.1), authors report a brief description of the output of > > . stcox protect, tvc(init_drug_level) texp(exp(-0.35*_t)) > > and highlight that > > "The hazard ratio 0.8848 is now interpreted as those with higher drug levels in their bloodstreams have a lower risk of having a hip fracture" > > as far as the time-varying variable is concerned. > > As a temptative hint, I was wondering whether the same approach - that is, splitting the Cox regression table to allow for constant-with-time variables (rh) and time-varying-variables (t) Hazard Ratios to be separately reported; describing in the Results section of your research report what causes your Hazard Ratios to differ - may match with your research aims. > > Sorry I cannot be more helpful. > > Kind Regards and All the Best for your research project, > Carlo > -----Messaggio originale----- > Da: owner-statalist@hsphsun2.harvard.edu [mailto:owner-statalist@hsphsun2.harvard.edu] Per conto di moleps islon > Inviato: venerdì 4 settembre 2009 9.43 > A: statalist@hsphsun2.harvard.edu > Oggetto: Re: st: R: reporting time dependent covariates in cox regression > > Appreciate your efforts, Carlo. However Lindholt et al had only > dichotomised before and after x yrs, not the -tvc-option in stata. I > agree that Cleves is a good book, but a little short on the practical > examples-and they dont either give any clues as to how results with > -tvc-should be presented. > > Regards, > M > > > On Fri, Sep 4, 2009 at 7:59 AM, Carlo Lazzaro<carlo.lazzaro@tin.it> wrote: >> Dear Moleps, >> As far as a part of your query is concerned >> >> <I still havent even seen anyone report whether PH assum_p_tions have been >> met, so I cant really find any articles to copy>. >> >> I would refer you to the following article: >> >> Jes S Lindholt, Svend Juul, Helge Fasting and Eskild W Henneberg >> Screening for abdominal aortic aneurysms: single centre randomised >> controlled trial. BMJ, doi:10.1136/bmj.38369.620162.82. (Particularly, >> Statistical analyses paragraph). >> >> For further details on Survival Analysis topics, I will recommend you to >> take a look at: >> Cleves MA, Gould WG, Gutierrez R. An Introduction To Survival Analysis Using >> Stata. Revised edition. College Station: StataPress, 2004; [ST] Stata >> manual. Survival analysis and epidemiological table. Release 9 >> >> Two other relevant contributors of the Statalist - Maarten Buis >> (http://home.fsw.vu.nl/m.buis/) and Stephen Jenkins >> (http://www.iser.essex.ac.uk/teaching/degree/stephenj/ec968/index.php.) >> published really interesting papers as well as teaching-notes on Survival >> Analysis. >> >> HTH and Kind Regards, >> >> Carlo >> >> -----Messaggio originale----- >> Da: owner-statalist@hsphsun2.harvard.edu >> [mailto:owner-statalist@hsphsun2.harvard.edu] Per conto di moleps islon >> Inviato: giovedě 3 settembre 2009 22.32 >> A: statalist@hsphsun2.harvard.edu >> Oggetto: st: reporting time dependent covariates in cox regression >> >> Dear listers, >> What is a good way to report time-dependent covariates in a >> multi-variate cox table? In my field of research I still havent even >> seen anyone report whether PH assumtions have been met, so I cant >> really find any articles to copy. >> >> I´m thinking in the lines of reporting an extra line beneath the main >> effect in parenthesis for the significant time-dependent variables in >> a table. Anyone have any experience in this? >> >> Also-how would you interpret a significant interaction between age and >> treatment that is also time-dependent? >> >> >> Regards, >> M >> >> * >> * 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/ >> > > * > * 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/

**Follow-Ups**:**R: R: st: R: reporting time dependent covariates in cox regression***From:*"Carlo Lazzaro" <carlo.lazzaro@tin.it>

**References**:**Re: st: R: reporting time dependent covariates in cox regression***From:*moleps islon <moleps2@gmail.com>

**R: st: R: reporting time dependent covariates in cox regression***From:*"Carlo Lazzaro" <carlo.lazzaro@tin.it>

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