Survival Analysis in R for Public Health

Imperial College London via Coursera

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Overview

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 whats 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, youll learn how to take a data set from scratch, import it...

Syllabus

The Kaplan-Meier Plot

What is survival analysis? Youll see what it is, when to use it and how to run and interpret the most common descriptive survival analysis method, the Kaplan-Meier plot and its associated log-rank test for comparing the survival of two or more patient groups, e.g. those on different treatments. Youll learn about the key concept of censoring.

The Cox Model

This week youll get to know the most commonly used survival analysis method for incorporating not just one but multiple predictors of survival: Cox proportional hazards regression modelling. Youll learn about the key concepts of hazards and the risk set. From now and until the end of this course, therell be plenty of chance to run Cox models on data simulated from real patient-level records for people admitted to hospital with heart failure. Youll see why missing data and categorical variables can cause problems in regression models such as Cox.

The Multiple Cox Model

Youll extend the simple Cox model to the multiple Cox model. As preparation, youll run the essential descriptive statistics on your main variables. Then youll see what can happen with real-life public health data and learn some simple tricks to fix the problem.

The Proportionality Assumption

In this final part of the course, youll learn how to assess the fit of the model and test the validity of the main assumptions involved in Cox regression such as proportional hazards. This will cover three types of residuals. Lastly, youll get to practise fitting a multiple Cox regression model and will have to decide which predictors to include and which to drop, a ubiquitous challenge for people fitting any type of regression model.