Dear Prof. Therneau
I was impressed to discover that the 'survConcordance' now handles Surv()
objects in counting format (example below to clarify what I mean). This is not
documented in the help page for the function. I am very curious to see how a
c-index is estimated in this case, using just the linear predictors. It was my
impression that with left truncation the ordering of individuals might change
over time as the baseline hazard is no longer monotonic. So ordering based on
just the linear predictors would not be appropriate but risk to t should be
used (after choosing a t). Am I wrong?
Example:
lung$time2=lung$time/365
lung2=na.omit(lung)
# the usual c-index
fit1 <- coxph(Surv(time, status) ~ ph.ecog+ph.karno+pat.karno+meal.cal+wt.loss
+ age + sex, lung2)
survConcordance(Surv(time, status) ~predict(fit1), lung2)
# and the corresponding c-index for start, stop data (counting)
fit2 <- coxph(Surv(age,age+time2, status) ~ ph.ecog
+ph.karno+pat.karno+meal.cal+wt.loss + age + sex, lung2)
survConcordance(Surv(time, status) ~predict(fit2), lung2)
Many thanks
Eleni Rapsomaniki
Research Associate
Department of Public Health and Primary Care
University of Cambridge
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