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#### 100% online

Start instantly and learn at your own schedule.

#### Intermediate Level

We advise that you first take the previous courses in the series, particularly Introduction to Statistics, though this is not essential.

#### 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

#### 100% online

Start instantly and learn at your own schedule.

#### Intermediate Level

We advise that you first take the previous courses in the series, particularly Introduction to Statistics, though this is not essential.

#### English

Subtitles: English

### Syllabus - What you will learn from this course

Week
1
4 hours to complete

## The Kaplan-Meier Plot

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.

...
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
About Imperial College & the team10m
How to be successful in this course10m
Data set and glossary10m
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
2 hours to complete

## The Cox Model

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.

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

## The Multiple Cox Model

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.

...
1 video (Total 6 min), 7 readings, 1 quiz
1 video
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
3 hours to complete

## The Proportionality Assumption

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.

...
3 videos (Total 11 min), 7 readings, 3 quizzes
3 videos
Cox proportional hazards assumption4m
Summary of Course2m
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

## Instructor

### Alex Bottle

School of Public Health

## 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.

Imperial College London is a world top ten university with an international reputation for excellence in science, engineering, medicine and business. located in the heart of London. Imperial is a multidisciplinary space for education, research, translation and commercialisation, harnessing science and innovation to tackle global challenges. Imperial students benefit from a world-leading, inclusive educational experience, rooted in the College’s world-leading research. Our online courses are designed to promote interactivity, learning and the development of core skills, through the use of cutting-edge digital technology....

## 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....