Survival Analysis in R
Survival Analysis in R, available at $64.99, has an average rating of 4.3, with 48 lectures, based on 433 reviews, and has 2184 subscribers.
You will learn about 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 This course is ideal for individuals who are Analysts working with survival data or Data scientists interested in this sub discipline of statistics or Medical researches and clinical trials personnel or Engineers and people in academia working with time event data or Students taking classes in survival analysis or related topics It is particularly useful for Analysts working with survival data or Data scientists interested in this sub discipline of statistics or Medical researches and clinical trials personnel or Engineers and people in academia working with time event data or Students taking classes in survival analysis or related topics.
Enroll now: Survival Analysis in R
Summary
Title: Survival Analysis in R
Price: $64.99
Average Rating: 4.3
Number of Lectures: 48
Number of Published Lectures: 48
Number of Curriculum Items: 48
Number of Published Curriculum Objects: 48
Original Price: $89.99
Quality Status: approved
Status: Live
What You Will 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
Who Should Attend
- 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
Target Audiences
- 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
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.
Course Curriculum
Chapter 1: Introduction
Lecture 1: Welcome to the Course: Survival Analysis in R
Lecture 2: Course Structure and Content: Managing Expectations
Lecture 3: The Survival Analysis Task View
Lecture 4: Survival Analysis Background
Lecture 5: Understanding Censored Data
Lecture 6: Course Script: Survival Analysis Models
Lecture 7: The Optimal Survival Dataset Structure and Our Main Course Dataset for Download
Chapter 2: General Survival Analysis Models
Lecture 1: Welcome to the Section: Non-Parametric Models for Survival Data
Lecture 2: The Survival Function
Lecture 3: The Survival Object
Lecture 4: The Kaplan-Meier Estimator
Lecture 5: Kaplan-Meier Plot
Lecture 6: Kaplan-Meier Plot with 'ggfortify'
Lecture 7: The Logrank Test
Lecture 8: Implementation of the Logrank Test in R
Lecture 9: Exercise: Kaplan-Meier Estimator and Logrank Test
Lecture 10: Solution: Kaplan-Meier Estimator and Logrank Test
Chapter 3: Cox Proportional Hazards Model and Parametric Models
Lecture 1: The Cox Proportional Hazards Model
Lecture 2: Implementation of the Cox Proportional Hazards Model in R
Lecture 3: Interpretation of the Model Result
Lecture 4: Aalen's Additive Regression Model
Lecture 5: Parametric Models in Survival Analysis
Lecture 6: Parametric Regression Models in Survival Analysis
Lecture 7: Exercise: Cox Proportional Hazards Model
Lecture 8: Solution: Cox Proportional Hazards Model
Chapter 4: Tree Based Models
Lecture 1: Survival Trees
Lecture 2: Survival Trees in R with Ranger
Lecture 3: Survival Tree Setup
Lecture 4: Visualizing the Survival Model
Lecture 5: Comparison Plot
Chapter 5: Managing the Time Variable in a Survival Dataset
Lecture 1: Tools for Date and Time Data in R
Lecture 2: Course Script: Managing the Time Variable
Lecture 3: Working with Dates and Time in R
Lecture 4: Format Conversion from Strings to Date/ Time
Lecture 5: The Lubridate Package
Lecture 6: Exercise
Lecture 7: Calculations with Lubridate
Lecture 8: Calculating Interval Length
Chapter 6: Outlier Detection and Missing Value Imputation in Survival Analysis
Lecture 1: Outlier Detection and Missing Data Imputation
Lecture 2: Missing Data Handling
Lecture 3: Course Script: Missing Data Handling and Outlier Detection
Lecture 4: Simple Methods for Missing Data Handling
Lecture 5: Missing Data Implementation with Machine Learning
Lecture 6: Statistical Outliers
Lecture 7: Detecting Outliers in Univariate Datasets
Lecture 8: Detecting Outliers in Multivariate Datasets
Lecture 9: Exercise: Missing Data Imputation and Outlier Detection
Lecture 10: Solution: Missing Data Imputation and Outlier Detection
Instructors
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R-Tutorials Training
Data Science Education
Rating Distribution
- 1 stars: 3 votes
- 2 stars: 14 votes
- 3 stars: 67 votes
- 4 stars: 151 votes
- 5 stars: 198 votes
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