Estimating and visualizing a survival curve
Let's take a look at the survival of breast cancer patients.
In this exercise, we work with the GBSG2
dataset again.
The survival
and survminer
packages and the GBSG2
data are loaded for you in this exercise.
This exercise is part of the course
Survival Analysis in R
Exercise instructions
- Estimate a survivor function for the breast cancer patients.
- Visualize the estimated survival function using the function
ggsurvplot()
. - Add a risk table to the plot showing the number of patients under observation. This can be done using the
risk.table
argument. - Add a line showing the median survival time to the plot. This can be done using the
surv.median.line
argument.
Hands-on interactive exercise
Have a go at this exercise by completing this sample code.
# Kaplan-Meier estimate
km <- survfit(Surv(___, ___) ~ ___, data = ___)
# Plot of the Kaplan-Meier estimate
ggsurvplot(___)
# Add the risk table to plot
ggsurvplot(___, ___ = TRUE)
# Add a line showing the median survival time
ggsurvplot(___, ___ = TRUE, ___ = "hv")