You can do statistical tests within a single model, for whether portions of it fit or do
not fit. But one cannot take three separate fits and compare them. The program needs
context to know how the three relate to one another. Say that "group" is your strata
variable, trt the variable of interest, and x1, x2 are adjusters.
fit <- coxph(Surv(time,status) ~ trt * strata(group) + x1 + x2, data=mydata)
Will fit a model with a separate treatment coefficient for each of the groups, and a
separate baseline hazard for each. One can now create a contrast that corresponds to your
trend test, using vcov(fit) for the variance matrix and coef(fit) to retrieve the
coefficients.
Terry T.
On 04/15/2014 05:00 AM, r-help-requ...@r-project.org wrote:
Hello,
I have the following problem. I stratified my patient cohort into three
ordered groups and performed multivariate adjusted Cox regression analysis
on each group separately. Now I would like to calculate a p for trend across
the hazard ratios that I got for the three groups. How can I do that if I
only have the HR and the confidence interval? For example I got the
following HRs for one endpoint:
1.09(0.68-1.74), 1.29(0.94-1.76) and 1.64(1.01-2.68).
There is a trend but how do I calculate if it is significant?
Best regards
Marcus Kleber
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