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!
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4 Hours14 Videos50 Exercises7,456 Learners
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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.
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  2. 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.
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  3. 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.
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  4. 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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In the following tracks
Statistician
Collaborators
David CamposRichie CottonShon Inouye
Heidi Seibold Headshot

Heidi Seibold

Statistician at LMU Munich
Heidi is a statistics postdoc at LMU Munich. Her research focus is on statistical methods for personalized 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 R related topics.
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