Survival Analysis in R from Udemy

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.

What's inside

Learning objectives

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
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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
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Show less

Syllabus

Introduction

Welcome to the Course: Survival Analysis in R

Course Structure and Content: Managing Expectations

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

Welcome to the Section: Non-Parametric Models for Survival Data

The Survival Function

The Survival Object

The Kaplan-Meier Estimator

Kaplan-Meier Plot

Kaplan-Meier Plot with 'ggfortify'

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

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

Survival Trees

Survival Trees in R with Ranger

Survival Tree Setup

Visualizing the Survival Model

Comparison Plot

Managing the Time Variable in a Survival Dataset

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

Exercise

Calculations with Lubridate

Calculating Interval Length

Outlier Detection and Missing Value Imputation in Survival Analysis

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

Good to know

Know what's good

, what to watch for

, and possible dealbreakers

Teaches survival analysis in R, which is a widely used technique in industries that work with event-driven data, such as finance, insurance, and healthcare

Taught by an experienced instructor with a background in both survival analysis and R programming

Covers a range of survival analysis models, from the basics like Kaplan-Meier and Cox proportional hazards to more advanced methods such as survival trees

Includes practical exercises and datasets to help learners apply their knowledge and skills to real-world scenarios

Provides guidance on handling and preparing survival data, which is crucial for accurate analysis

Recommended for students and professionals with some prior knowledge of statistics and R programming

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Survival Analysis in R with these activities:

Compile a collection of survival analysis resources

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Compiling a collection of survival analysis resources will make it easy for you to find the information you need when you need it.

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Collect links to survival analysis resources.
Organize the resources into a logical structure.
Create a document or spreadsheet that contains the resources.

Read a book on survival analysis

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Reading a book on survival analysis will provide you with a comprehensive overview of the field.

View Survival Analysis on Amazon

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Find a book on survival analysis.
Read the book and complete the exercises.
Apply what you learned to your own data.

Create a cheat sheet of survival analysis terminology

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Creating a cheat sheet of survival analysis terminology will help you remember the key terms and concepts of survival analysis.

Browse courses on Survival Analysis

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List the key terms and concepts of survival analysis.
Define each term and concept in your own words.
Create a cheat sheet that summarizes the terms and concepts.

Five other activities

Expand to see all activities and additional details

Show all eight activities

Follow a tutorial on using the survival package in R

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Following a tutorial on using the survival package in R will help you learn the basics of survival analysis in R.

Browse courses on R

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Find a tutorial on using the survival package in R.
Follow the tutorial and complete the exercises.
Apply what you learned to your own data.

Recreate a survival model from a paper

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Recreating a survival model from a paper helps you understand the model's structure and assumptions, and allows you to apply your own data to the model.

Browse courses on Survival Models

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Find a paper that presents a survival model.
Read the paper and understand the model's structure and assumptions.
Implement the model in R using the survival package.
Apply the model to your own data.
Compare your results to the results in the paper.

Practice fitting survival models to simulated data

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Practicing fitting survival models to simulated data will help you gain experience with the different types of survival models and how to interpret their results.

Browse courses on Survival Models

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Compare the performance of the different models.
Generate simulated survival data.
Fit different types of survival models to the data.
Interpret the results of the models.

Attend a workshop on survival analysis

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Attending a workshop on survival analysis will allow you to learn from experts in the field and get hands-on experience with survival analysis techniques.

Browse courses on Survival Analysis

Show steps

Find a workshop on survival analysis.
Register for the workshop.
Attend the workshop and participate in the activities.

Start a project that uses survival analysis

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Starting a project that uses survival analysis will give you the opportunity to apply your knowledge to a real-world problem.

Browse courses on Survival Analysis

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Define the problem that you want to solve.
Collect data that is relevant to the problem.
Fit a survival model to the data.
Interpret the results of the model.
Write a report on your findings.

Career center

Learners who complete Survival Analysis in R will develop knowledge and skills that may be useful to these careers:

Statistician

Statisticians apply mathematical and statistical methods to collect, analyze, interpret, and present data. Survival analysis is a specialized field of statistics that deals with the analysis of time-to-event data. The course, Survival Analysis in R, provides comprehensive training in the methods and techniques used in survival analysis, making it a valuable resource for Statisticians.

See salaries and explore the career path for Statistician

Biostatistician

Biostatisticians apply statistical methods to solve problems in the field of biology and medicine. Survival analysis is a specialized field of statistics that deals with the analysis of time-to-event data. The course, Survival Analysis in R, provides comprehensive training in the methods and techniques used in survival analysis, making it a valuable resource for Biostatisticians.

See salaries and explore the career path for Biostatistician

Data Scientist

Data Scientists use their knowledge of mathematics, statistics, and computer science to extract insights from data. Survival analysis is a specialized field of statistics that deals with the analysis of time-to-event data. The course, Survival Analysis in R, provides comprehensive training in the methods and techniques used in survival analysis, making it a valuable resource for Data Scientists working in the healthcare and pharmaceutical industries.

See salaries and explore the career path for Data Scientist

Medical Scientist

Medical Scientists typically need a doctoral degree to qualify for employment. They apply their knowledge of biology, chemistry, and medicine to research and develop cures for diseases. The course, Survival Analysis in R, can help Medical Scientists to analyze survival data which can help them to identify risk factors for disease and to develop more effective treatments.

See salaries and explore the career path for Medical Scientist

Epidemiologist

Epidemiologists study the distribution and patterns of health events and diseases in a population. Survival analysis can help Epidemiologists to identify risk factors for disease and to develop more effective prevention and treatment strategies. The course, Survival Analysis in R provides valuable training in the methods and techniques used in survival analysis, making it a valuable resource for Epidemiologists.

See salaries and explore the career path for Epidemiologist

Actuary

Actuaries use mathematical and statistical methods to assess risk and uncertainty. Survival analysis is a specialized field of statistics that deals with the analysis of time-to-event data. The course, Survival Analysis in R, provides comprehensive training in the methods and techniques used in survival analysis, making it a valuable resource for Actuaries working in the insurance and financial industries.

See salaries and explore the career path for Actuary

Clinical Research Associate

Clinical Research Associates manage and coordinate clinical trials. Survival analysis is a specialized field of statistics that deals with the analysis of time-to-event data. The course, Survival Analysis in R, provides valuable training in the methods and techniques used in survival analysis, making it a valuable resource for Clinical Research Associates involved in oncology and other therapeutic areas.

See salaries and explore the career path for Clinical Research Associate

Pharmacokineticist

Pharmacokineticists study the absorption, distribution, metabolism, and excretion of drugs in the body. Survival analysis is a specialized field of statistics that deals with the analysis of time-to-event data. The course, Survival Analysis in R, provides valuable training in the methods and techniques used in survival analysis, making it a valuable resource for Pharmacokineticists involved in drug development.

See salaries and explore the career path for Pharmacokineticist

Data Analyst

Data Analysts collect, clean, and analyze data to extract insights. Survival analysis is a specialized field of statistics that deals with the analysis of time-to-event data. The course, Survival Analysis in R, may be useful for Data Analysts who need to understand and analyze survival data in their work.

See salaries and explore the career path for Data Analyst

Healthcare Consultant

Healthcare Consultants provide advice and guidance to healthcare organizations on a variety of topics, including data analysis. Survival analysis is a specialized field of statistics that deals with the analysis of time-to-event data. The course, Survival Analysis in R, may be useful for Healthcare Consultants who need to understand and analyze survival data in their work.

See salaries and explore the career path for Healthcare Consultant

Medical Writer

Medical Writers create and edit scientific and medical documents. Survival analysis is a specialized field of statistics that deals with the analysis of time-to-event data. The course, Survival Analysis in R, may be useful for Medical Writers who need to understand and communicate survival data in their writing.

See salaries and explore the career path for Medical Writer

Business Analyst

Business Analysts use data to solve business problems. Survival analysis is a specialized field of statistics that deals with the analysis of time-to-event data. The course, Survival Analysis in R, may be useful for Business Analysts who need to understand and analyze survival data in their work.

See salaries and explore the career path for Business Analyst

Market Research Analyst

Market Research Analysts study market trends and customer behavior. Survival analysis is a specialized field of statistics that deals with the analysis of time-to-event data. The course, Survival Analysis in R, may be useful for Market Research Analysts who need to understand and analyze customer behavior over time.

See salaries and explore the career path for Market Research Analyst

Regulatory Affairs Specialist

Regulatory Affairs Specialists ensure that medical products meet regulatory requirements. Survival analysis is a specialized field of statistics that deals with the analysis of time-to-event data. The course, Survival Analysis in R, may be useful for Regulatory Affairs Specialists who need to understand and analyze survival data in their work.

See salaries and explore the career path for Regulatory Affairs Specialist

Software Engineer

Software Engineers design, develop, and maintain software applications. Survival analysis is a specialized field of statistics that deals with the analysis of time-to-event data. The course, Survival Analysis in R, may be useful for Software Engineers who need to develop software applications that analyze survival data.

See salaries and explore the career path for Software Engineer