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From |
Robert Davidson <rhd773@gmail.com> |

To |
statalist@hsphsun2.harvard.edu |

Subject |
Re: st: stcox question |

Date |
Thu, 29 Mar 2012 17:47:09 -0400 |

Maarten, Thank you for your detailed reponse; it is quite helpful. Best Regards, Rob, On Wed, Mar 28, 2012 at 4:05 AM, Maarten Buis <maartenlbuis@gmail.com> wrote: > On Tue, Mar 27, 2012 at 4:46 PM, Robert Davidson wrote: >> I am estimating several hazard models, using stcox, using the by >> command to separate by whether or not the the observations (people) >> have criminal records. >> I am estimating a standard model: by crime, sort: stcox (varlist) and >> clustering the standard errors. >> I would like to test whether some of my coefficients/hazard rates (of >> variables in varlist) for one type (say those with criminal records) >> are significantly larger than for the other type. Is there a way I >> can do this that does not involve running the model on the full sample >> and creating an interaction term (criminal record * var x)? I would >> like to avoid all of the issues that arise with interaction >> coefficients in binary models as people in my area are quite skeptical >> of the interpretation of such interactions. I know I can estimate a >> logit model and use the Norton et al. correction for the interaction, >> but I would like to find a more convincing way to test this difference >> across models. > > However you are going to estimate this, there is just no getting > around the fact that what you want to estimate is an interaction > effect. But things are not as bad as you think. The Norton et al. > correction only applies to marginal effects, if you interpret your > model in the natural metric of the model than there is no need for a > correction. This is particularly relevant for -stcox- as there is no > meaningful marginal effect for that model. > > The logic behind Cox regression is that it estimates hazard rate > ratios (note: this is _not_ the same as hazard rate) without > estimating the baseline hazard function. This is a strength in the > sense that you cannot make mistakes in things you don't estimate, but > it is also a weakness in the sense that you can only interpret those > hazard ratios; all other statistics you might be interested in but > require the baseline hazard in order to compute it (including marginal > effects) cannot be computed from the results of a Cox model. Moreover, > I don't think marginal effects make any sense within the context of > survival analysis: you have the usual problem that there can be > substantial variation in marginal effects between observation and on > top of that there can be substantial variation in marginal effects > within an observation over time. > > So with a Cox regression you are going to interpret your coefficients > within the natural metric of that model (hazard rate ratios) because > marginal effects cannot be computed and even if they could they would > make no sense. Since you are using the natural metric there is no need > for any corrections, so it is easy. In other words: I would just do > the interaction model and interpret the exponentiated interaction > terms as ratios of hazard rate ratios. A link to various examples > including Cox regression is given here: > <http://www.maartenbuis.nl/publications/interactions.html> > > Hope this helps, > Maarten > > -------------------------- > Maarten L. Buis > Institut fuer Soziologie > Universitaet Tuebingen > Wilhelmstrasse 36 > 72074 Tuebingen > Germany > > > http://www.maartenbuis.nl > -------------------------- > > * > * 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: stcox question***From:*Robert Davidson <rhd773@gmail.com>

**Re: st: stcox question***From:*Maarten Buis <maartenlbuis@gmail.com>

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