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

Survival Analysis in R for Public Health

This course is part of Statistical Analysis with R for Public Health Specialization

Taught in English

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Alex Bottle

Instructor: Alex Bottle

13,849 already enrolled

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Course

Gain insight into a topic and learn the fundamentals

4.5

(303 reviews)

|

91%

Intermediate level

Recommended experience

11 hours (approximately)
Flexible schedule
Learn at your own pace
Prepare for a degree

What you'll learn

  • Run Kaplan-Meier plots and Cox regression in R and interpret the output

  • Describe a data set from scratch, using descriptive statistics and simple graphical methods as a necessary first step for more advanced analysis

  • Describe and compare some common ways to choose a multiple regression model

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Assessments

9 quizzes

Course

Gain insight into a topic and learn the fundamentals

4.5

(303 reviews)

|

91%

Intermediate level

Recommended experience

11 hours (approximately)
Flexible schedule
Learn at your own pace
Prepare for a degree

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This course is part of the Statistical Analysis with R for Public Health Specialization
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There are 4 modules in this course

What is survival analysis? You’ll 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. You’ll learn about the key concept of censoring.

What's included

4 videos11 readings3 quizzes2 discussion prompts1 plugin

This week you’ll 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. You’ll learn about the key concepts of hazards and the risk set. From now and until the end of this course, there’ll be plenty of chance to run Cox models on data simulated from real patient-level records for people admitted to hospital with heart failure. You’ll see why missing data and categorical variables can cause problems in regression models such as Cox.

What's included

3 videos4 readings2 quizzes1 discussion prompt

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

What's included

1 video7 readings1 quiz2 discussion prompts

In this final part of the course, you’ll 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, you’ll 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.

What's included

3 videos7 readings3 quizzes1 discussion prompt1 plugin

Instructor

Instructor ratings
4.7 (60 ratings)
Alex Bottle
Imperial College London
6 Courses62,526 learners

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4.5

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DN
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