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st: interaction term between categorical and continuous variable in survival analysis


From   moleps islon <moleps2@gmail.com>
To   statalist@hsphsun2.harvard.edu
Subject   st: interaction term between categorical and continuous variable in survival analysis
Date   Wed, 9 Sep 2009 00:49:05 +0200

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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