Hello,
I would like, if it is possible, to compare the effect of a variable across
regression models. I have looked around but I haven't found anything. Maybe
someone could help? Here is the problem:
I am studying the effect of a variable (age) on an outcome (local recurrence:
lr). I have built 3 models:
- model 1: lr ~ age y = \beta_(a1).age
- model 2: lr ~ age + presentation variables (X_p) y = \beta_(a2).age +
\BETA_(p2).X_p
- model 3: lr ~ age + presentation variables + treatment variables( X_t)
y = \beta_(a3).age + \BETA_(p3).X_(p) + \BETA_(t3).X_t
Presentation variables include variables such as tumor grade, tumor size,
etc...
the physician cannot interfer with these variables.
Treatment variables include variables such as chemotherapy, radiation, surgical
margins (a surrogate for adequate surgery).
I have used cph for the models and restricted cubic splines (Design library)
for
age. I have noted that the effect of age decreases from model 1 to 3.
I would like to compare the effect of age on the outcome across the different
models. A test of \beta_(a1) = \beta_(a2) = \beta_(a3) and then two by two
comparisons or a global trend test maybe? Is that possible?
Thank you for your help,
David Biau.
[[alternative HTML version deleted]]
______________________________________________
R-help@r-project.org mailing list
https://stat.ethz.ch/mailman/listinfo/r-help
PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
and provide commented, minimal, self-contained, reproducible code.
