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Survival Analysis in R for Public Health

Approx. 11 hours to complete

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Course Summary

Learn how to analyze data for public health studies using survival analysis techniques in R. This course covers the basics of survival analysis and how it can be applied to public health studies.

Key Learning Points

Understand the basics of survival analysis and how it can be applied to public health studies
Learn how to use R to perform survival analysis
Discover advanced topics such as time-varying covariates and competing risks

Job Positions & Salaries of people who have taken this course might have

Public Health Analyst
- USA: $62,000 - $96,000
- India: INR 4,50,000 - INR 11,00,000
- Spain: €30,000 - €45,000
Epidemiologist
- USA: $60,000 - $122,000
- India: INR 3,00,000 - INR 17,00,000
- Spain: €26,000 - €51,000
Data Analyst
- USA: $50,000 - $94,000
- India: INR 2,50,000 - INR 9,00,000
- Spain: €24,000 - €42,000

Learning Outcomes

Understand the basics of survival analysis and its applications in public health
Learn how to use R to perform survival analysis
Be able to analyze and interpret survival data in public health studies

Prerequisites or good to have knowledge before taking this course

Basic knowledge of statistics
Familiarity with R programming language

Course Difficulty Level

Intermediate

Course Format

Online
Self-paced
Video lectures

Similar Courses

Introduction to Data Analysis for Public Health
Epidemiology: The Basic Science of Public Health

Related Education Paths

Notable People in This Field

Father of Epidemiology
Founder of Modern Nursing

Related Books

Description

Welcome to Survival Analysis in R for Public Health!

The three earlier courses in this series covered statistical thinking, correlation, linear regression and logistic regression. This one will show you how to run survival – or “time to event” – analysis, explaining what’s meant by familiar-sounding but deceptive terms like hazard and censoring, which have specific meanings in this context. Using the popular and completely free software R, you’ll learn how to take a data set from scratch, import it into R, run essential descriptive analyses to get to know the data’s features and quirks, and progress from Kaplan-Meier plots through to multiple Cox regression. You’ll use data simulated from real, messy patient-level data for patients admitted to hospital with heart failure and learn how to explore which factors predict their subsequent mortality. You’ll learn how to test model assumptions and fit to the data and some simple tricks to get round common problems that real public health data have. There will be mini-quizzes on the videos and the R exercises with feedback along the way to check your understanding. Prerequisites Some formulae are given to aid understanding, but this is not one of those courses where you need a mathematics degree to follow it. You will need basic numeracy (for example, we will not use calculus) and familiarity with graphical and tabular ways of presenting results. The three previous courses in the series explained concepts such as hypothesis testing, p values, confidence intervals, correlation and regression and showed how to install R and run basic commands. In this course, we will recap all these core ideas in brief, but if you are unfamiliar with them, then you may prefer to take the first course in particular, Statistical Thinking in Public Health, and perhaps also the second, on linear regression, before embarking on this one.

Knowledge

Run Kaplan-Meier plots and Cox regression in R and interpret the output
Describe a data set from scratch, using descriptive statistics and simple graphical methods as a necessary first step for more advanced analysis
Describe and compare some common ways to choose a multiple regression model

Outline

The Kaplan-Meier Plot
Welcome to Course
What is Survival Analysis?
The KM plot and Log-rank test
What is Heart Failure and How to run a KM plot in R
About Imperial College & the team
How to be successful in this course
Grading policy
Data set and glossary
Additional Readings
Life tables
Feedback: Life Tables
The Course Data Set
Feedback: Running a KM plot and log-rank test
Practice in R: Run another KM Plot and log-rank test
Feedback: Running another KM plot and log-rank test
Survival Analysis Variables
Life tables
Practice in R: Running a KM plot and log-rank test

The Cox Model
Intro to Cox Model
How to run Simple Cox model in R
Introduction to Missing Data
Hazard Function and Risk Set
Practice in R: Simple Cox Model
Feedback: Simple Cox Model
Further Reading
Hazard function and Ratio
Simple Cox Model

The Multiple Cox Model
Interpreting the output from multiple Cox model
Introduction to Running Descriptives
Practice in R: Getting to know your data
Feedback: Getting to know your data
How to run multiple Cox model in R
Introduction to Non-convergence
Practice: Fixing the problem of non-convergence
Feedback on fixing a non-converging model
Multiple Cox Model

The Proportionality Assumption
How to assess Cox model fit
Cox proportional hazards assumption
Summary of Course
Checking the proportionality assumption
Feedback on Practice Quiz
What to do if the proportionality assumption is not met
How to choose predictors for a regression model
Results of the exercise on model selection and backwards elimination
Final Code
Assessing the proportionality assumption in practice
Testing the proportionality assumption with another variable
End-of-Module Assessment

Summary of User Reviews

Learn how to analyze survival data in the context of public health with this comprehensive course on survival analysis in R. Students have praised the course's practical approach and real-world examples.

Key Aspect Users Liked About This Course

The course's practical approach and real-world examples

Pros from User Reviews

Easy to follow and understand
Great instructor and teaching style
Real-world examples make the course applicable to work
Comprehensive coverage of survival analysis

Cons from User Reviews

Some sections may be too basic for advanced users
No quizzes or assignments to reinforce learning
Course material may need to be supplemented with additional resources

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