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
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.
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