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.
Este ejercicio forma parte del curso
Survival Analysis in R
Instrucciones del ejercicio
- 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.tableargument. - Add a line showing the median survival time to the plot. This can be done using the
surv.median.lineargument.
Ejercicio interactivo práctico
Prueba este ejercicio y completa el código de muestra.
# 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")