Back to Survival Analysis in R for Public Health
Imperial College London

Survival Analysis in R for Public Health

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.

Status: Predictive Modeling
Status: R (Software)
IntermediateCourse11 hours

Featured reviews

YX

4.0Reviewed Nov 22, 2019

The final quiz is a little bit confusing ,pls provide detailed feedback on it so we can learn further even we did not pass it.

FA

5.0Reviewed Jul 22, 2019

Very nice introductory course on survival analysis in R. Exercises were well designed.

RR

5.0Reviewed Dec 31, 2020

The course is wonderful and very informative. Only feedback is the final quiz was pretty hard.

AP

5.0Reviewed Jun 17, 2023

This a good course for those who want to dive into survival analysis.

VA

4.0Reviewed Jan 30, 2021

Good intro, just wish there would be an intro to more advanced methods (e.g. time varying covariates).

VD

5.0Reviewed Aug 27, 2019

Good and practical introduction to survival analysis. I liked the emphasis on how to deal with practical data sets and data problems.

LY

5.0Reviewed Aug 24, 2020

What a great course it is!!! I could get the solid basic knowledge from the course.

ZK

4.0Reviewed Nov 26, 2024

An excellent introductory course. I hope they offer an advanced version of this specialization as well.

AB

5.0Reviewed Jun 15, 2020

Awesome course learned a lot from this entire series. Thank you!!!

KM

5.0Reviewed May 30, 2020

Very enjoyable course, and simple but effective application using R which I know very well in my practice

LH

4.0Reviewed Jun 4, 2020

the use of R in the course was immersive and enjoyable, although the way some assignments were presented was inconsistent at times.

RP

5.0Reviewed Dec 29, 2020

Very well explained including aspects not commonly covered in other tutorials such as assumptions testing.

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