HomeRSurvival Analysis in R

# Survival Analysis in R

4.2+
13 reviews
Intermediate

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 Hours14 Videos50 Exercises
11,730 LearnersStatement of Accomplishment

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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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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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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.

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.

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 with R

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Collaborators

Prerequisites

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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## Don’t just take our word for it

*4.2
from 13 reviews
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• Dimitris L.
5 months

nice course

• Nicolas F.
5 months

This was a fantastic course to learn a package that is capable of producing great analyses typically applicable in the medical sciences.

• Edwin A.
5 months

Through this course I have learned to work with time-to-event data, to estimate using Kaplan-Meier estimate, visualize and interpret survival models, such as Weibull and Cox model.

• Inaamul H.
8 months

The course was a good introduction to survival analysis. The best part of DataCamp courses is that you get to use the code and try the analysis in the finely built exercises.

• Andriej P.
11 months

A smooth yet insightful introduction into survival analysis. Made it simple and comprehandable.

"nice course"

Dimitris L.

"This was a fantastic course to learn a package that is capable of producing great analyses typically applicable in the medical sciences."

Nicolas F.

"Through this course I have learned to work with time-to-event data, to estimate using Kaplan-Meier estimate, visualize and interpret survival models, such as Weibull and Cox model."

Edwin A.