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: Aalens 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

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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