Modeling time to death in cancer both age and treatment (binary) have
a clearly significant effect;
stcox age tx
_t | Haz. Ratio Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
age | 1.023254 .0040953 5.74 0.000 1.015259 1.031312
tx | .4005361 .0407233 -9.00 0.000 .3281696 .4888605
However I´d like to check for the interaction between the two:
gen age_tx=age*tx
stcox age tx age_tx
_t | Haz. Ratio Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
age | 1.026575 .0049524 5.44 0.000 1.016915 1.036328
tx | .7671728 .3931694 -0.52 0.605 .28097 2.094723
age_tx| .9886413 .0087311 -1.29 0.196 .9716759 1.005903
So my model can now be simplified to B1(age)+tx(B2+B3*age). However as
long as both B2 and B3 are p>0.05 how do I interpret this? Should I
use lincom tx+age_tx?
. lincom tx+age_tx,hr
------------------------------------------------------------------------------
_t | Haz. Ratio Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
(1) | .7584587 .3821458 -0.55 0.583 .2825258 2.036131
------------------------------------------------------------------------------
Intuitively I´d say that this new beta is rather similar to the
original tx beta and that age doesnt matter for treatment here, but I
really dont understand exactly what this linear combination of tx and
age_tx parameter is telling me?
Anyone care for an explanation?
Regards,
M
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