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From |
"Lachenbruch, Peter" <Peter.Lachenbruch@oregonstate.edu> |

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

Subject |
RE: st: reporting cox regression for ordinal variables |

Date |
Mon, 6 Oct 2008 10:34:05 -0700 |

In the command xi: stcox a, one is entering the variable a as a linear effect. If one wanted a as a categorical predictor one would write xi: stcox i.a I may have missed an earlier (or later) post on this. Sorry if I'm redundant. Tony Peter A. Lachenbruch Department of Public Health Oregon State University Corvallis, OR 97330 Phone: 541-737-3832 FAX: 541-737-4001 -----Original Message----- From: owner-statalist@hsphsun2.harvard.edu [mailto:owner-statalist@hsphsun2.harvard.edu] On Behalf Of Maarten buis Sent: Sunday, October 05, 2008 3:24 PM To: statalist@hsphsun2.harvard.edu Subject: Re: st: reporting cox regression for ordinal variables --- moleps islon <moleps2@gmail.com> wrote: > I'm writing up a paper on survival in cancer. However I've got two > ordinal variables. I've done the univariate analysis using xi: stcox > a, and then tested for linear trend using testparm and discarded one > of the variables due to p>0.5. However one of my variables (ecog) > comes back highly significant using - testparm -. > In a regular cox table how would you report this? Would you report > each and every one of the levels of the ordinal variable with its > coefficient? Or can I just use the beta from stcox without xi'ing it > since I now know the variable has a linear relationship?? With or without -xi-ing means two different models: with -xi-ing you enter the variable non-linearly as a set of dummies, without -xi-ing you enter that variable linearly, in this case linear in the log(hazard ratio). Choosing which model to report is ultimately your choice, which means that you and only you are responsible and you cannot delegate that responsibility to Stata or any test. For instance, if you found that a linear model is not significantly different from a non-linear model than that could just means that your sample size is not big enough to detect any non-linearity, and if you find that the two models are significantly different than that could just mean that your sample size is so big that you detected irrelevant deviations from linearity. Either way the test result are inconclusive. I would limit testing to the hypotheses I really care about, and build my model such that it includes at least all the variables I am interested in, even if they are insignificant, and maybe some controls (though keep in mind to include only possible confounding variables and not to include intervening variables). I would decide whether or not to enter a variable linearly or as a set of dummies using a graph, like the graph below. (Notice the inconsistency in my argument here as I include confidence intervals in the graph. What can I say: I am only human.) Also when entering a variable linearly you should think very carefully about the spacing of the categories: do you have any information that might help you give these categories more realistic values, are these categories evenly spaced, etc? *------------- begin example ------------------ sysuse cancer, clear gen cat_age = cond(age <= 50, 0, /// cond(age <= 60, 1, 2)) stset studytime, failure(died) xi: stcox i.drug i.cat_age est store a xi: stcox i.drug cat_age est store b lrtest a b est restore a xi i.cat_age i.drug adjust _Idrug_2=0 _Idrug_3=0, by(cat_age) ci replace est restore b adjust _Idrug_2=0 _Idrug_3=0, by(cat_age) ci replace twoway scatter xb cat_age || /// rcap lb ub cat_age || /// line _xb cat_age, /// legend(off) /// xlab(0 1 2) /// ytitle("log(hazard ratio)") *----------------- end example ----------------------- (For more on how to use examples I sent to the Statalist, see http://home.fsw.vu.nl/m.buis/stata/exampleFAQ.html ) Hope this helps, Maarten ----------------------------------------- Maarten L. Buis Department of Social Research Methodology Vrije Universiteit Amsterdam Boelelaan 1081 1081 HV Amsterdam The Netherlands visiting address: Buitenveldertselaan 3 (Metropolitan), room N515 +31 20 5986715 http://home.fsw.vu.nl/m.buis/ ----------------------------------------- * * 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**:**st: reporting cox regression for ordinal variables***From:*"moleps islon" <moleps2@gmail.com>

**Re: st: reporting cox regression for ordinal variables***From:*Maarten buis <maartenbuis@yahoo.co.uk>

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