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Survival Analysis in R

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4 hours
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.
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In the following Tracks

  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.

    Play Chapter Now
    The term "survival analysis"
    50 xp
    Introducing the GBSG2 dataset
    100 xp
    What will this course cover?
    50 xp
    Why learn survival methods?
    50 xp
    Digging into the GBSG2 dataset 1
    100 xp
    Using the Surv() function for GBSG2
    100 xp
    The UnempDur dataset
    100 xp
    Measures used in survival analysis
    50 xp
    Interpreting a survival curve I
    50 xp
    Interpreting a survival curve II
    50 xp
    Interpreting a survival curve III
    50 xp
  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.

    Play Chapter Now
    Kaplan-Meier estimate
    50 xp
    Function to compute the Kaplan-Meier estimate
    50 xp
    First Kaplan-Meier estimate
    100 xp
    When does the Kaplan-Meier curve drop?
    50 xp
    Why use Kaplan-Meier
    50 xp
    Understanding and visualizing Kaplan-Meier curves
    50 xp
    Exercise ignoring censoring
    100 xp
    Estimating and visualizing a survival curve
    100 xp
    The Weibull model for estimating survival curves
    50 xp
    Estimating median survival from a Weibull model
    100 xp
    Survival curve quantiles from a Weibull model
    100 xp
    Estimating the survival curve with survreg()
    100 xp
    Visualizing the results of Weibull models
    50 xp
    Comparing Weibull model and Kaplan-Meier estimate
    100 xp
  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.

    Play Chapter Now
    Why use the Weibull model
    50 xp
    Interpreting coefficients
    100 xp
    Compute Weibull model
    100 xp
    Visualizing Weibull models
    50 xp
    ggsurvplot() versus ggsurvplot_df()
    50 xp
    Computing a Weibull model and the survival curves
    100 xp
    Visualising a Weibull model
    100 xp
    Plotting options
    50 xp
    Other distributions than Weibull
    50 xp
    survreg() arguments
    50 xp
    Computing a Weibull and a log-normal model
    100 xp
    Comparing Weibull and Log-Normal Model I
    100 xp
    Comparing Weibull and Log-Normal Model II
    100 xp
  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.

    Play Chapter Now
    The Cox model
    50 xp
    Computing a Cox model
    100 xp
    Proportional hazards assumption
    50 xp
    Visualizing the Cox model
    50 xp
    Computing the survival curve from a Cox model
    100 xp
    Visualizing a Cox model
    100 xp
    surv_summary()
    50 xp
    What we've learned
    50 xp
    Why "imaginary patients"?
    50 xp
    Capstone: The Cox model
    100 xp
    Capstone: Comparing survival curves
    100 xp
    Good bye
    50 xp
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GroupTraining 2 or more people?

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In the following Tracks

Collaborators

Collaborator's avatar
David Campos
Collaborator's avatar
Shon Inouye
Collaborator's avatar
Richie Cotton

Prerequisites

Introduction to Regression in R
Heidi Seibold HeadshotHeidi Seibold

Statistician / Research & Education Ambassador, IGDORE

Heidi is an independent researcher with IGDORE and research and education ambassador at Johner Institut. Her research is on the intersection of data science, open science and medicine. Heidi has collaborated on several R packages and was reproducibility editor for the Journal of Statistical Software. 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. In her free time she likes to cycle.
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