Den 2019-08-22 kl. 21:48, skrev Göran Broström:
On 2019-08-18 19:10, Ferenci Tamas wrote:
Dear All,
Consider the following simple example:
library( survival )
data( veteran )
coef( coxph(Surv(time, status) ~ trt + prior + karno, data = veteran) )
trt prior karno
0.180197194 -0.005550919 -0.033771018
Note that we have neither time-dependent covariates, nor time-varying
coefficients, so the results should be the same if we change to
counting process format, no matter where we cut the times.
That's true if we cut at event times:
veteran2 <- survSplit( Surv(time, status) ~ trt + prior + karno,
data = veteran, cut = unique( veteran$time ) )
coef( coxph(Surv(tstart,time, status) ~ trt + prior + karno, data =
veteran2 ) )
trt prior karno
0.180197194 -0.005550919 -0.033771018
But quite interestingly not true, if we cut at every day:
veteran3 <- survSplit( Surv(time, status) ~ trt + prior + karno,
data = veteran, cut = 1:max(veteran$time) )
coef( coxph(Surv(tstart,time, status) ~ trt + prior + karno, data =
veteran3 ) )
trt prior karno
0.180197215 -0.005550913 -0.033771016
The difference is not large, but definitely more than just a rounding
error, or something like that.
What's going on? How can the results get wrong, especially by
including more cutpoints?
All results are wrong, but they are useful (paraphrasing George EP Box).
That said, it is a little surprising: The generated risk sets are
(should be) identical in all cases, and one would expect rounding errors
to be the same. But data get stored differently, and ... who knows?
I tried your examples on my computer and got exactly the same results as
you. Which surprised me.
G,
Göran
Thank you in advance,
Tamas
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