--- On Sun, 30/8/09, moleps islon wrote:
> I´m investigating the effect of a dichotomous variable on
> survival. Looking at the graphs they are parallell after
> the first 48 hrs, but there is a difference within the
> first 48 hrs. Is it reasonable to present the cox
> regression analysis with all the patients and then
> discard the patients dead within 48 hrs and redo the
> analysis-(this gives a significant effect in the first
> analysis and non-significant in the latter as suspected
> from the graphs)? Or is there a more analytical way to find
> the exact time from which the two groups no longer differ?
I would do something like in the example below. In this example
the effect of age is allowed to change linearly over time for
the first 10 months, and afterwards the proportional hazard
assumption is maintained. The trick is that -(_t < 10)- is a
logical statement, so it evaluates to 1 if it is true, and 0
if it is false (for more on this, see:
http://www.stata.com/support/faqs/data/trueorfalse.html ). So
for the first 10 months age is interacted with 1*_t, while for
the remaining months age is interacted with 0*_t. The effect
age in "rh equation" is the effect of age after 10 months, i.e.
after 10 months the hazard of death increased 12% for every
year of age. The effect of age in t equation tells you that in
the first 10 months the effect of age increased every month by
.0033%.
*--------- begin example -----------
sysuse cancer, clear
stset studytime, failure(died)
xi: stcox i.drug age, ///
tvc(age) texp((_t < 10)*_t)
*---------- end example -------------
( For more on how to use examples I sent to statalist see:
http://www.maartenbuis.nl/stata/exampleFAQ.html )
Hope this helps,
Maarten
-----------------------------------------
Maarten L. Buis
Institut fuer Soziologie
Universitaet Tuebingen
Wilhelmstrasse 36
72074 Tuebingen
Germany
http://www.maartenbuis.nl/
-----------------------------------------
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