Survival Analysis in R

Master survival analysis, duration analysis, time-to-event analysis, or reliability analysis using R

English name of the course: Survival Analysis in R

This video tutorial is 1.9GB in total, with Chinese and English subtitles, clear picture quality without watermark, and full source code attachments

Course address: https://xueshu.fun/1499 Demo address: https://www.udemy.com/course/survival-analysis-in-r/

Course content

what will you learn

General Concepts of Survival Analysis
How to Use R for Survival Analysis
Optimal package for determining survival data
The best data structure for a survival dataset and how to clean it
Visualize survival models using different graphing tools: ggplot2 , ggfortify , R Base
Kaplan-Meier estimator
log rank test
Cox proportional hazards model
parametric model
survival tree
imputation of missing data
Outlier Detection
Working with date and time data using lubridate

This course includes:

4 hours video on demand
3 articles
2 downloadable assets
Access on mobile and TV

Require

Required programs: R and RStudio
Basic R skills
Interest in Survival Analysis

describe

Survival analysis is a subdiscipline 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 for performing such analyzes thanks to the survival package.

In this course, you will learn how to perform survival analysis using R. To view course content, it is recommended to view the class schedule. There are also videos available for free preview.

The course structure is as follows:

We'll start with course orientation, background on packages primarily used for survival analysis and how to find them, course datasets, and general survival analysis concepts.

After that, we'll jump right in and create our first survival model. We will use the Kaplan Meier estimator and the log- rank test as our first standard survival analysis tools.

When we talk about survival analysis, there is one model type that 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 fairly new machine learning tools are gaining popularity in survival analysis. In R, you can fit such a survival tree using several functions.

The last two sections of the course are designed to get your dataset ready for analysis. In many cases, you'll find that datetime data needs to be properly formatted for use. Therefore, I added a dedicated section on datetime handling , focusing on the lubridate package. You will also learn how to detect and replace missing values and outliers . These problematic data can completely disrupt your analysis, so understanding how to manage them is critical.

In addition to videos, code, and datasets, you also have access to a lively discussion board dedicated to survival analysis.

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

More than 100,000 people around the world have mastered data science through our courses. Why don't you try it yourself? With Udemy's 30-day money-back guarantee , you have nothing to lose and only gain valuable skills to stand out in today's job market.

Who this course is suitable for:

analyst who analyzes survival data
Data scientists interested in this sub-discipline of statistics
Medical Research and Clinical Trials Staff
Engineers and academics working with temporal event data
Students taking courses in survival analysis or related topics

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