Interactive Course

Survival Analysis in R

Learn to work with time-to-event data. The event may be death or finding a job after unemployment. Learn to estimate, visualize, and interpret survival models!

  • 4 hours
  • 14 Videos
  • 50 Exercises
  • 3,098 Participants
  • 3,650 XP

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Course Description

Do patients taking the new drug survive longer than others? How fast do people get a new job after getting unemployed? What can I do to make my friends stay on the dancefloor at my party? All these questions require the analysis of time-to-event data, for which we use special statistical methods. This course introduces basic concepts of time-to-event data analysis, also called survival analysis. Learn how to deal with time-to-event data and how to compute, visualize and interpret survivor curves as well as Weibull and Cox models.

  1. 1

    What is Survival Analysis?

    Free

    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. 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.

  3. 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.

  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.

  1. 1

    What is Survival Analysis?

    Free

    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.

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Heidi Seibold
Heidi Seibold

Statistician at LMU Munich

Heidi is a statistics postdoc at LMU Munich. Her research focus is on statistical methods for personalised medicine with the aim of improving treatment of patients. Heidi has collaborated on several R packages and is an assistant editor for the Journal of Statistical Software, where she is responsible for reproducibility checks. She promotes open and reproducible science and sees R and Git as some of the most powerful tools for computational reproducibility in statistics and machine learning. Heidi loves to teach, especially R related topics.

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Collaborators
  • Richie Cotton

    Richie Cotton

  • David Campos

    David Campos

  • Shon Inouye

    Shon Inouye

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