Monthly Subscription Starting at AED 99 + VAT

Survival Analysis in R for Public Health
  • Level Professional
  • Duration 12 hours
  • Course by Imperial College London
  • Offered by

About

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.

Modules

Welcome to Imperial College London

    • 5 Readings
    • 1 Discussion
    • 1 Plugin
Show info about module content

1 Discussions

  • Nice to meet you!

5 Readings

  • About Imperial College & the team
  • How to be successful in this course
  • Grading policy
  • Data set and glossary
  • Additional Readings

Kaplan-Meier plots and log-rank test

    • 4 Videos
    • 6 Readings
    • 1 Discussion
    • 3 Assignment
Show info about module content

3 Assignment

  • Survival Analysis Variables
  • Life tables
  • Practice in R: Running a KM plot and log-rank test

1 Discussions

  • Share and Reflect: What experience do you have of Survival Analysis?

4 Videos

  • 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

6 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

The simple Cox model

    • 3 Videos
    • 4 Readings
    • 1 Discussion
    • 2 Assignment
Show info about module content

2 Assignment

  • Hazard function and Ratio
  • Simple Cox Model

1 Discussions

  • Share and Reflect: Simple Cox Model

3 Videos

  • Intro to Cox Model
  • How to run Simple Cox model in R
  • Introduction to Missing Data

4 Readings

  • Hazard Function and Risk Set
  • Practice in R: Simple Cox Model
  • Feedback: Simple Cox Model
  • Further Reading

The multiple Cox model

    • 1 Videos
    • 7 Readings
    • 2 Discussion
    • 1 Assignment
Show info about module content

1 Assignment

  • Multiple Cox Model

2 Discussions

  • Share and Reflect: Getting to know your data
  • Practice in R: Running a multiple Cox model that doesn't converge

1 Videos

  • Interpreting the output from multiple Cox model

7 Readings

  • 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

Testing model assumptions and choosing predictors

    • 3 Videos
    • 7 Readings
    • 1 Discussion
    • 1 Plugin
    • 3 Assignment
Show info about module content

3 Assignment

  • Assessing the proportionality assumption in practice
  • Testing the proportionality assumption with another variable
  • End-of-Module Assessment

1 Discussions

  • Issues you encountered during the model selection exercise

3 Videos

  • How to assess Cox model fit
  • Cox proportional hazards assumption
  • Summary of Course

7 Readings

  • 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
  • Practice in R: Running a Multiple Cox Model
  • Results of the exercise on model selection and backwards elimination
  • Final Code

Auto Summary

"Survival Analysis in R for Public Health" is designed for health and fitness professionals. Led by expert instructors, it focuses on "time to event" analysis using R software, covering Kaplan-Meier plots to multiple Cox regression. The course includes practical data sets, quizzes, and feedback, spanning 720 minutes. Subscription options include Starter and Professional, ideal for those with a basic understanding of statistical concepts and public health data analysis.

Alex Bottle
Instructor

Alex Bottle