Survival Analysis in R
4.3 (156 ratings)
Course Ratings are calculated from individual students’ ratings and a variety of other signals, like age of rating and reliability, to ensure that they reflect course quality fairly and accurately.
797 students enrolled

Survival Analysis in R

Use R to master survival analysis, duration analysis, event time analysis or reliability analysis.
4.3 (156 ratings)
Course Ratings are calculated from individual students’ ratings and a variety of other signals, like age of rating and reliability, to ensure that they reflect course quality fairly and accurately.
797 students enrolled
Last updated 4/2019
English [Auto]
Current price: $72.99 Original price: $104.99 Discount: 30% off
5 hours left at this price!
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This course includes
  • 4 hours on-demand video
  • 3 articles
  • 2 downloadable resources
  • Full lifetime access
  • Access on mobile and TV
  • Certificate of Completion
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What you'll learn
  • The general concepts of survival analysis
  • How to use R for survival analysis
  • Identify the best packages for survival data
  • The best data structure of a survival dataset and how to clean it
  • Visualizing survival models with different charting tools: ggplot2, ggfortify, R Base
  • Kaplan-Meier estimator
  • Logrank test
  • Cox proportional hazards model
  • Parametric models
  • Survival trees
  • Missing data imputation
  • Outlier detection
  • Date and time data handling with lubridate
Course content
Expand all 48 lectures 03:55:55
+ Introduction
7 lectures 31:21
The Survival Analysis Task View
Survival Analysis Background
Understanding Censored Data
Course Script: Survival Analysis Models
The Optimal Survival Dataset Structure and Our Main Course Dataset for Download
+ General Survival Analysis Models
10 lectures 38:13
Welcome to the Section: Non-Parametric Models for Survival Data
The Survival Function
The Survival Object
The Kaplan-Meier Estimator
Kaplan-Meier Plot
The Logrank Test
Implementation of the Logrank Test in R
Exercise: Kaplan-Meier Estimator and Logrank Test
Solution: Kaplan-Meier Estimator and Logrank Test
+ Cox Proportional Hazards Model and Parametric Models
8 lectures 39:18
The Cox Proportional Hazards Model
Implementation of the Cox Proportional Hazards Model in R
Interpretation of the Model Result
Aalen's Additive Regression Model
Parametric Models in Survival Analysis
Parametric Regression Models in Survival Analysis
Exercise: Cox Proportional Hazards Model
Solution: Cox Proportional Hazards Model
+ Tree Based Models
5 lectures 22:16
Survival Trees
Survival Tree Setup
Visualizing the Survival Model
Comparison Plot
+ Managing the Time Variable in a Survival Dataset
8 lectures 40:05
Tools for Date and Time Data in R
Course Script: Managing the Time Variable
Working with Dates and Time in R
Format Conversion from Strings to Date/ Time
The Lubridate Package
Calculations with Lubridate
Calculating Interval Length
+ Outlier Detection and Missing Value Imputation in Survival Analysis
10 lectures 01:04:41
Outlier Detection and Missing Data Imputation
Missing Data Handling
Course Script: Missing Data Handling and Outlier Detection
Simple Methods for Missing Data Handling
Missing Data Implementation with Machine Learning
Statistical Outliers
Detecting Outliers in Univariate Datasets
Detecting Outliers in Multivariate Datasets
Exercise: Missing Data Imputation and Outlier Detection
Solution: Missing Data Imputation and Outlier Detection
  • Required programs: R and RStudio
  • Basic R skills
  • Interest in survival analysis

Survival Analysis is a sub discipline of statistics. It actually has several names. In some fields it is called event-time analysis, reliability analysis or duration analysis. R is one of the main tools to perform this sort of analysis thanks to the survival package.

In this course you will learn how to use R to perform survival analysis. To check out the course content it is recommended to take a look at the course curriculum. There are also videos available for free preview.

The course structure is as follows:

We will start out with course orientation, background on which packages are primarily used for survival analysis and how to find them, the course datasets as well as general survival analysis concepts.

After that we will dive right in and create our first survival models. We will use the Kaplan Meier estimator as well as the logrank test as our first standard survival analysis tools.

When we talk about survival analysis there is one model type which is an absolute cornerstone of survival analysis: the Cox proportional hazards model. You will learn how to create such a model, how to add covariates and how to interpret the results.

You will also learn about survival trees. These rather new machine learning tools are more and more popular in survival analysis. In R you have several functions available to fit such a survival tree.

The last 2 sections of the course are designed to get your dataset ready for analysis. In many scenarios you will find that date-time data needs to be properly formatted to even work with it. Therefore, I added a dedicated section on date-time handling with a focus on the lubridate package. And you will also learn how to detect and replace missing values as well as outliers. These problematic pieces of data can totally destroy your analysis, therefore it is crucial to understand how to manage it.

Besides the videos, the code and the datasets, you also get access to a vivid discussion board dedicated to survival analysis.

By the way, this course is part of a whole data science course portfolio. Check out the R-Tutorials instructor page to see all the other available course.

Well over 100.000 people around the world did already use our classes to master data science. Why don´t you try it out yourself? With a Udemy 30-day money back guarantee there is nothing you can lose, you can only gain precious skills to come out ahead in today’s job market.

Who this course is for:
  • Analysts working with survival data
  • Data scientists interested in this sub discipline of statistics
  • Medical researches and clinical trials personnel
  • Engineers and people in academia working with time event data
  • Students taking classes in survival analysis or related topics