Why does Survival curve sum up to 100% when less than 50% experience event?

问题

This problem has confounded me for more hours than I care to admit. I have isolated the problem so I can replicate it.

library(survival)
library(survminer)

set.seed(123)
test <- data.frame(rnorm(10000)+5,
                   sample(0:1, 10000, replace = TRUE))

colnames(test)<- c("time", "event")
#sum(test$event) = 4975
survfitted <- survfit(Surv(time = time, event = event) ~ 1,
                      data = test)
plot(survfitted, fun = "event")

Why does this curve sum up to 100% when only 49.75% experience an event? What would be the right syntax for producing a plot showing the cumulative incidence proportion?

The problem appears to be that the censoring is treated as an event.

回答1:

If the censoring events all occur before the last event, then the the last event will take the KM-curve to 0, or as in this case will take the Hazard curve to 1.0. (The plot is a KM estimate of Hazard rather than of Survival.)

Your simulation distributed the events and censoring extremely evenly, so almost any such plot will show the Hazard function approaching very close to 1. If you chose your seed as 9 , you get a plot where it does not quite reach zero.

set.seed(9)
png(); plot(survfitted, fun = "event"); abline(h=1);dev.off()

The Hazard plot will always get close to 1 if the events and censoring times are distributed evenly across the same range. The reason that most medical examples of survival or hazard plots terminate in the middle of hte 0-1 range is that typically there are many censoring times out beyond the last observed event.

来源：https://stackoverflow.com/questions/60757904/why-does-survival-curve-sum-up-to-100-when-less-than-50-experience-event