本课程是 Statistical Analysis with R for Public Health 专项课程 专项课程的一部分

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课程信息

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.

立即开始，按照自己的计划学习。

根据您的日程表重置截止日期。

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

建议：3-5 hours/week...

字幕：英语（English）

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

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

立即开始，按照自己的计划学习。

根据您的日程表重置截止日期。

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

建议：3-5 hours/week...

字幕：英语（English）

周

1What 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 个视频 （总计 16 分钟）, 11 个阅读材料, 3 个测验

What is Survival Analysis?4分钟

The KM plot and Log-rank test4分钟

What is Heart Failure and How to run a KM plot in R4分钟

About Imperial College & the team10分钟

How to be successful in this course10分钟

Grading policy10分钟

Data set and glossary10分钟

Additional Readings10分钟

Life tables20分钟

Feedback: Life Tables10分钟

The Course Data Set20分钟

Feedback: Running a KM plot and log-rank test3分钟

Practice in R: Run another KM Plot and log-rank test10分钟

Feedback: Running another KM plot and log-rank test10分钟

Survival Analysis Variables30分钟

Life tables30分钟

Practice in R: Running a KM plot and log-rank test20分钟

周

2This 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 个视频 （总计 18 分钟）, 4 个阅读材料, 2 个测验

Hazard Function and Risk Set20分钟

Practice in R: Simple Cox Model30分钟

Feedback: Simple Cox Model10分钟

Further Reading20分钟

Hazard function and Ratio5分钟

Simple Cox Model15分钟

周

3You’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 个视频 （总计 6 分钟）, 7 个阅读材料, 1 个测验

Introduction to Running Descriptives10分钟

Practice in R: Getting to know your data30分钟

Feedback: Getting to know your data10分钟

How to run multiple Cox model in R20分钟

Introduction to Non-convergence10分钟

Practice: Fixing the problem of non-convergence10分钟

Feedback on fixing a non-converging model15分钟

Multiple Cox Model10分钟

周

4In 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 个视频 （总计 11 分钟）, 7 个阅读材料, 3 个测验

Checking the proportionality assumption10分钟

Feedback on Practice Quiz10分钟

What to do if the proportionality assumption is not met20分钟

How to choose predictors for a regression model20分钟

Practice in R: Running a Multiple Cox Model

Results of the exercise on model selection and backwards elimination10分钟

Final Code10分钟

Assessing the proportionality assumption in practice5分钟

Testing the proportionality assumption with another variable15分钟

End-of-Module Assessment20分钟

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....

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....

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