# 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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## 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
### 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.

The term "survival analysis"50 xpIntroducing the GBSG2 dataset100 xpWhat will this course cover?50 xpWhy learn survival methods?50 xpDigging into the GBSG2 dataset 1100 xpUsing the Surv() function for GBSG2100 xpThe UnempDur dataset100 xpMeasures used in survival analysis50 xpInterpreting a survival curve I50 xpInterpreting a survival curve II50 xpInterpreting a survival curve III50 xp - 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.

Kaplan-Meier estimate50 xpFunction to compute the Kaplan-Meier estimate50 xpFirst Kaplan-Meier estimate100 xpWhen does the Kaplan-Meier curve drop?50 xpWhy use Kaplan-Meier50 xpUnderstanding and visualizing Kaplan-Meier curves50 xpExercise ignoring censoring100 xpEstimating and visualizing a survival curve100 xpThe Weibull model for estimating survival curves50 xpEstimating median survival from a Weibull model100 xpSurvival curve quantiles from a Weibull model100 xpEstimating the survival curve with survreg()100 xpVisualizing the results of Weibull models50 xpComparing Weibull model and Kaplan-Meier estimate100 xp - 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.

Why use the Weibull model50 xpInterpreting coefficients100 xpCompute Weibull model100 xpVisualizing Weibull models50 xpggsurvplot() versus ggsurvplot_df()50 xpComputing a Weibull model and the survival curves100 xpVisualising a Weibull model100 xpPlotting options50 xpOther distributions than Weibull50 xpsurvreg() arguments50 xpComputing a Weibull and a log-normal model100 xpComparing Weibull and Log-Normal Model I100 xpComparing Weibull and Log-Normal Model II100 xp - 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.

The Cox model50 xpComputing a Cox model100 xpProportional hazards assumption50 xpVisualizing the Cox model50 xpComputing the survival curve from a Cox model100 xpVisualizing a Cox model100 xpsurv_summary()50 xpWhat we've learned50 xpWhy "imaginary patients"?50 xpCapstone: The Cox model100 xpCapstone: Comparing survival curves100 xpGood bye50 xp

In the following tracks

StatisticianPrerequisites

Introduction to Regression in R#### Heidi 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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