Dear R users,
My question is more methodology related rather than specific to R usage. Using
time on study as time in a cox model, eg:
library(Design)
stanf.cph1=cph(Surv(time, status) ~ t5+id+age, data=stanford2, surv=T)
#In this case the 1000-day survival probability would be:
stanf.surv1=survest(stanf.cph1, times=1000)
#Age in this case is a covariate.
#I now want to compare the above estimate to the 1000-day survival probability
I get using age at entry and exit as my time-scale:
stanf.cph2=cph(Surv(age,age+time, status) ~ t5+id, data=stanford2, surv=T)
stanf.surv2=survest(stanf.cph2, times=1000)
summary(stanf.surv1$surv)
Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
0.1131 0.3370 0.4669 0.4538 0.5633 0.7480 27.0000
> summary(stanf.surv2$surv)
Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
0.07387 0.23240 0.35770 0.35370 0.46820 0.60650 27.00000
These are obviously out-of sync, so there must be some way I can adjust them to
mean the same thing. The first means the probability of surviving a 1000 days
since they started being followed up while the second means the probability of
surviving up to starting age+1000 days. How do I get the equivalent risks from
the two models?
Any tips greatly appreciated!!
(FYI A related entry to my question can be found at:
http://tolstoy.newcastle.edu.au/R/e2/help/07/02/9831.html)
Eleni Rapsomaniki
Research Associate
Department of Public Health and Primary Care
University of Cambridge
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