Survival Analysis in R for Public Health

Offered byCoursera
Public/Government Institute

Survival Analysis in R for Public Health
at
Coursera
Overview

Duration	11 hours
Total fee	Free
Mode of learning	Online
Difficulty level	Intermediate
Official Website	Explore Free Course
Credential	Certificate

Table of content

Overview
Highlights
Course Details
Curriculum

Survival Analysis in R for Public Health
at
Coursera
Highlights

Shareable Certificate Earn a Certificate upon completion
100% online Start instantly and learn at your own schedule.
Course 4 of 4 in the Statistical Analysis with R for Public Health Specialization
Flexible deadlines Reset deadlines in accordance to your schedule.
Intermediate Level We advise that you first take the previous courses in the series, particularly Introduction to Statistics, though this is not essential.
Approx. 11 hours to complete
English Subtitles: French, Portuguese (European), Russian, English, Spanish

Survival Analysis in R for Public Health
at
Coursera
Course details

Skills you will learn

R Regression Hypothesis Testing Statistics Software Testing

More about this course

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.