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: Descriptive Statistics
Status: Biostatistics
IntermediateCourse11 hours

Featured reviews

FA

5.0Reviewed Jul 22, 2019

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

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.

AB

5.0Reviewed Jun 15, 2020

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

LY

5.0Reviewed Aug 24, 2020

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

SP

5.0Reviewed Nov 7, 2019

Excellent course. Definitely a MUST DO if you would like to learn statistics in RStudio.

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.

DN

5.0Reviewed Mar 10, 2023

This class is so interesting it helped me to add more knowledge in survival analysis in the case of R.

LA

5.0Reviewed Jul 3, 2020

Great course superb support and very clear professor. This course is a good motivator to continue to explore public health and statistics.

RR

5.0Reviewed Dec 31, 2020

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

RP

5.0Reviewed Dec 29, 2020

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

AM

5.0Reviewed Oct 25, 2024

This was an excellent course, The explanations were so plain and good. Huge thanks to the instructor - Alex Bottle to making this course.

MW

5.0Reviewed Dec 26, 2019

Take this course alongwith linear and logistic regression in R

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