Survival Analysis in R for Public Health

Description

Alex Bottle

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.

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Fast Facts

Tools
R

Techniques
Applied MathematicsConfidence IntervalsCorrelationData AnalysisData ModelingData SetsHealthcare AnalyticsRegressionStatistical AnalysisSurvival AnalysisVariables

Similar Opportunities
Mixed Models with R

Online Textbooks

Linear Regression in R for Public Health

Coursera - Imperial College London

Logistic Regression in R for Public Health

Coursera - Imperial College London

Modeling Data in the Tidyverse

Coursera - Johns Hopkins University