About this Course

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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: English

What you will 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

Skills you will gain

Understand common ways to choose what predictors go into a regression modelRun and interpret Kaplan-Meier curves in RConstruct a Cox regression model in R
Shareable Certificate
Earn a Certificate upon completion
100% online
Start instantly and learn at your own schedule.
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: English

Offered by

Imperial College London logo

Imperial College London

Start working towards your Master's degree

This course is part of the 100% online Global Master of Public Health from Imperial College London. If you are admitted to the full program, your courses count towards your degree learning.

Syllabus - What you will learn from this course

Week
1

Week 1

4 hours to complete

The Kaplan-Meier Plot

4 hours to complete
4 videos (Total 16 min), 11 readings, 3 quizzes
4 videos
What is Survival Analysis?4m
The KM plot and Log-rank test4m
What is Heart Failure and How to run a KM plot in R4m
11 readings
About Imperial College & the team10m
How to be successful in this course10m
Grading policy10m
Data set and glossary10m
Additional Readings10m
Life tables20m
Feedback: Life Tables10m
The Course Data Set20m
Feedback: Running a KM plot and log-rank test3m
Practice in R: Run another KM Plot and log-rank test10m
Feedback: Running another KM plot and log-rank test10m
3 practice exercises
Survival Analysis Variables30m
Life tables30m
Practice in R: Running a KM plot and log-rank test20m
Week
2

Week 2

2 hours to complete

The Cox Model

2 hours to complete
3 videos (Total 18 min), 4 readings, 2 quizzes
3 videos
How to run Simple Cox model in R7m
Introduction to Missing Data5m
4 readings
Hazard Function and Risk Set20m
Practice in R: Simple Cox Model30m
Feedback: Simple Cox Model10m
Further Reading20m
2 practice exercises
Hazard function and Ratio5m
Simple Cox Model15m
Week
3

Week 3

2 hours to complete

The Multiple Cox Model

2 hours to complete
1 video (Total 6 min), 7 readings, 1 quiz
7 readings
Introduction to Running Descriptives10m
Practice in R: Getting to know your data30m
Feedback: Getting to know your data10m
How to run multiple Cox model in R20m
Introduction to Non-convergence10m
Practice: Fixing the problem of non-convergence10m
Feedback on fixing a non-converging model15m
1 practice exercise
Multiple Cox Model10m
Week
4

Week 4

3 hours to complete

The Proportionality Assumption

3 hours to complete
3 videos (Total 11 min), 7 readings, 3 quizzes
3 videos
Cox proportional hazards assumption4m
Summary of Course2m
7 readings
Checking the proportionality assumption10m
Feedback on Practice Quiz10m
What to do if the proportionality assumption is not met20m
How to choose predictors for a regression model20m
Practice in R: Running a Multiple Cox Model
Results of the exercise on model selection and backwards elimination10m
Final Code10m
3 practice exercises
Assessing the proportionality assumption in practice5m
Testing the proportionality assumption with another variable15m
End-of-Module Assessment20m

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About the Statistical Analysis with R for Public Health Specialization

Statistics are everywhere. The probability it will rain today. Trends over time in unemployment rates. The odds that India will win the next cricket world cup. In sports like football, they started out as a bit of fun but have grown into big business. Statistical analysis also has a key role in medicine, not least in the broad and core discipline of public health. In this specialisation, you’ll take a peek at what medical research is and how – and indeed why – you turn a vague notion into a scientifically testable hypothesis. You’ll learn about key statistical concepts like sampling, uncertainty, variation, missing values and distributions. Then you’ll get your hands dirty with analysing data sets covering some big public health challenges – fruit and vegetable consumption and cancer, risk factors for diabetes, and predictors of death following heart failure hospitalisation – using R, one of the most widely used and versatile free software packages around. This specialisation consists of four courses – statistical thinking, linear regression, logistic regression and survival analysis – and is part of our upcoming Global Master in Public Health degree, which is due to start in September 2019. The specialisation can be taken independently of the GMPH and will assume no knowledge of statistics or R software. You just need an interest in medical matters and quantitative data....
Statistical Analysis with R for Public Health

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