1. Apprendre
    2. /
  2. Cours
    3. /
  3. Survival Analysis in R
  • 1

    What is Survival Analysis?

    Gratuit

    In the first chapter, we introduce the concept of survival analysis, explain the importance of this topic, and provide a quick introduction to the theory behind survival curves. We discuss why special methods are needed when dealing with time-to-event data and introduce the concept of censoring. We also discuss how we describe the distribution of the elapsed time until an event.

  • 2

    Estimation of survival curves

    In this chapter, we will look into different methods of estimating survival curves. We will discuss the Kaplan-Meier estimate and the Weibull model as tools for survival curve estimation and learn how to communicate those results through visualization.

  • 3

    The Weibull model

    In this chapter, we will learn how to estimate and visualize a Weibull model to learn about the effects of covariates on the time-to-event outcome.

  • 4

    The Cox Model

    In the last chapter, we learn how to compute and interpret Cox models to understand why they are useful and how they differ from Weibull models.

Initializing

Exercice

Comparing Weibull and Log-Normal Model II

In this exercise, we want to compare the survival curves estimated by a Weibull model and by a log-normal model for the GBSG2 data. This exercise shows how the estimates change if you use a different distribution.

The survival, survminer, and reshape2 packages and the GBSG2 data are loaded for you in this exercise.

Instructions

100 XP
  • Plot the survival curves using the ggsurvplot_df() function. The linetype should correspond to the hormonal therapy and the color to the distribution (dist).