Hi:

Another possible approach (untested) would be to compare the two models

m1 <- coxph(Surv(Start, End, Death.ID) ~ x1 + x2 + a1 + a2 + a3)
m0 <- coxph(Surv(Start, End, Death.ID) ~ I(x1 + x2) + a1 + a2 + a3)
anova(m0, m1)

This should be able to test H_0: beta_1 = beta_2. If you want to test that
they are both equal to a specified [nonzero] constant, that's a different
test entirely (e.g., beta_1 = 1 = beta_2) - in that case, offset() might be
useful.

HTH,
Dennis

On Tue, Apr 12, 2011 at 2:19 AM, Michael Haenlein <haenlein@escpeurope.eu>wrote:

> Dear all,
>
> I'm running a coxph model of the form:
> coxph(Surv(Start, End, Death.ID) ~ x1 + x2 + a1 + a2 + a3)
>
> Within this model, I would like to compare the influence of x1 and x2 on
> the
> hazard rate.
> Specifically I am interested in testing whether the estimated coefficient
> for x1 is equal (or not) to the estimated coefficient for x2.
>
> I was thinking of using a Chow-test for this but the Chow test appears to
> work for linear regression only (see:
> http://en.wikipedia.org/wiki/Chow_test).
> Another option I was thinking of is to estimate an alternative model in
> which the coefficients for x1 and x2 are constraint to be equal and to
> compare the fit of such a constraint model with the one of an unconstraint
> one. But again I'm not sure how this can be done using coxph.
>
> Could anyone help me out on this please?
>
> Thanks,
>
> Michael
>
>
>
> Michael Haenlein
> Associate Professor of Marketing
> ESCP Europe
> Paris, France
>
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>
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