[R] Stats question - cox proportional hazards adjustments

Geoff Russell geoffrey.russell at gmail.com
Wed Sep 20 15:54:16 CEST 2006

Peter et al,

Thanks for the reply, I did reread the posting guide before posting and figured
it was a short question and might just have a short answer. I have
Therneau's book
on order, which will probably clarify the matter in time.

I understand stratifying to deal with confounding, but not adding it
as a covariate in a regression. e.g, If one of the gender
related effects you mention happens to be
drinking, then we don't want to "get rid of it", it may well
be an additional covariate and we want its full effect embodied in the
b value for
that covariate.

I'll keep reading!


On 20 Sep 2006 14:47:00 +0200, Peter Dalgaard <p.dalgaard at biostat.ku.dk> wrote:
> "Geoff Russell" <geoffrey.russell at gmail.com> writes:
> > Hi useRs,
> >
> > Many studies of the link between red meat and colorectal cancer use
> > Cox proportional
> > hazards with (among other things) a gender covariate.
> >
> > If it is true that men eat more red meat, drink more alcohol and smoke more than
> > women, and if it is also true that alcohol and tobacco are known risk
> > factors then why does
> > it make sense to "adjust" for gender?   I would think that in this
> > case some of the
> > risk that should be properly attributed to the bad habits will actually end
> > up being attributed to being male instead.
> This is more than a bit off-topic for the list, but in (very) brief:
> Because you need to get rid of purely gender related effects that
> disturb the analysis and may create spurious association.
> Otherwise you would become able to "prove" effects like stiletto heels
> causing breast cancer, etc.
> --
>    O__  ---- Peter Dalgaard             Øster Farimagsgade 5, Entr.B
>   c/ /'_ --- Dept. of Biostatistics     PO Box 2099, 1014 Cph. K
>  (*) \(*) -- University of Copenhagen   Denmark          Ph:  (+45) 35327918
> ~~~~~~~~~~ - (p.dalgaard at biostat.ku.dk)                  FAX: (+45) 35327907

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