First Kaplan-Meier estimate
In this exercise, we will use the same data shown in the video. We will take a look at the survfit()
function and the object it generates. This exercise will help you explore the survfit
object.
The survival
package is loaded for you in this exercise.
This exercise is part of the course
Survival Analysis in R
Exercise instructions
- Explore the use of the
survfit()
function by entering?survfit
in the console. - Compute the Kaplan-Meier estimate using
survfit()
. - Take a look at the structure of the
survfit
object usingstr()
. - Create a
data.frame
with the four time points, the corresponding number at risk (n.risk
), number of observations with an event (n.event
), number of observations censored (n.censor
) and the value of the survival curve (surv
). Take all info from thesurvfit
object.
Hands-on interactive exercise
Have a go at this exercise by completing this sample code.
# Create time and event data
time <- c(5, 6, 2, 4, 4)
event <- c(1, 0, 0, 1, 1)
# Compute Kaplan-Meier estimate
km <- survfit(___(___, ___) ~ ___)
km
# Take a look at the structure
str(___)
# Create data.frame
data.frame(time = km$time, n.risk = ____, n.event = ____,
n.censor = ____, surv = ___)