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From |
"Kieran McCaul" <Kieran.McCaul@uwa.edu.au> |

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

Subject |
st: RE: interaction term between categorical and continuous variable in survival analysis |

Date |
Wed, 9 Sep 2009 07:08:35 +0800 |

... With the interaction, the treatment effect, 0.767, is the effect at age=0. Assuming that you have entered age as age in years, this isn't very informative. You should centre the age. qui sum age, meanonly gen age2=age-`r(mean)' Now run the regressions using age2. You should get the same result in the model without the interaction. In the model with the interaction, the treatment effect will be the effect at the average age. The interaction term is not significant, which suggests that you should drop the interaction term, but you could use -lrtest- to check that there is no improvement in fit. Also, fitting age as a continuous variable assumes that the hazard ratio is log-linear with age. For common cancers in human populations, this is probably OK, but there are exceptions. Cancers in children, for example, or testicular cancer. ______________________________________________ Kieran McCaul MPH PhD WA Centre for Health & Ageing (M573) University of Western Australia Level 6, Ainslie House 48 Murray St Perth 6000 Phone: (08) 9224-2701 Fax: (08) 9224 8009 email: Kieran.McCaul@uwa.edu.au http://myprofile.cos.com/mccaul http://www.researcherid.com/rid/B-8751-2008 ______________________________________________ If you live to be one hundred, you've got it made. Very few people die past that age - George Burns -----Original Message----- From: owner-statalist@hsphsun2.harvard.edu [mailto:owner-statalist@hsphsun2.harvard.edu] On Behalf Of moleps islon Sent: Wednesday, 9 September 2009 6:49 AM To: statalist@hsphsun2.harvard.edu Subject: st: interaction term between categorical and continuous variable in survival analysis Modeling time to death in cancer both age and treatment (binary) have a clearly significant effect; stcox age tx _t | Haz. Ratio Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- age | 1.023254 .0040953 5.74 0.000 1.015259 1.031312 tx | .4005361 .0407233 -9.00 0.000 .3281696 .4888605 However I´d like to check for the interaction between the two: gen age_tx=age*tx stcox age tx age_tx _t | Haz. Ratio Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- age | 1.026575 .0049524 5.44 0.000 1.016915 1.036328 tx | .7671728 .3931694 -0.52 0.605 .28097 2.094723 age_tx| .9886413 .0087311 -1.29 0.196 .9716759 1.005903 So my model can now be simplified to B1(age)+tx(B2+B3*age). However as long as both B2 and B3 are p>0.05 how do I interpret this? Should I use lincom tx+age_tx? . lincom tx+age_tx,hr ------------------------------------------------------------------------------ _t | Haz. Ratio Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- (1) | .7584587 .3821458 -0.55 0.583 .2825258 2.036131 ------------------------------------------------------------------------------ Intuitively I´d say that this new beta is rather similar to the original tx beta and that age doesnt matter for treatment here, but I really dont understand exactly what this linear combination of tx and age_tx parameter is telling me? Anyone care for an explanation? 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/

**References**:**st: interaction term between categorical and continuous variable in survival analysis***From:*moleps islon <moleps2@gmail.com>

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