课程信息
专项课程
100% 在线

100% 在线

立即开始,按照自己的计划学习。
可灵活调整截止日期

可灵活调整截止日期

根据您的日程表重置截止日期。
中级

中级

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

完成时间(小时)

完成时间大约为9 小时

建议:3-5 hours/week...
可选语言

英语(English)

字幕:英语(English)

您将学到的内容有

  • Check

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

  • Check

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

  • Check

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

您将获得的技能

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
专项课程
100% 在线

100% 在线

立即开始,按照自己的计划学习。
可灵活调整截止日期

可灵活调整截止日期

根据您的日程表重置截止日期。
中级

中级

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

完成时间(小时)

完成时间大约为9 小时

建议:3-5 hours/week...
可选语言

英语(English)

字幕:英语(English)

教学大纲 - 您将从这门课程中学到什么

1
完成时间(小时)
完成时间为 4 小时

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....
Reading
4 个视频 (总计 16 分钟), 11 个阅读材料, 3 个测验
Video4 个视频
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
Reading11 个阅读材料
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
Quiz3 个练习
Survival Analysis Variables30分钟
Life tables30分钟
Practice in R: Running a KM plot and log-rank test20分钟
2
完成时间(小时)
完成时间为 2 小时

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....
Reading
3 个视频 (总计 18 分钟), 4 个阅读材料, 2 个测验
Video3 个视频
How to run Simple Cox model in R7分钟
Introduction to Missing Data5分钟
Reading4 个阅读材料
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
Quiz2 个练习
Hazard function and Ratio5分钟
Simple Cox Model15分钟
3
完成时间(小时)
完成时间为 2 小时

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....
Reading
1 个视频 (总计 6 分钟), 7 个阅读材料, 1 个测验
Reading7 个阅读材料
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
Quiz1 个练习
Multiple Cox Model10分钟
4
完成时间(小时)
完成时间为 3 小时

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....
Reading
3 个视频 (总计 11 分钟), 7 个阅读材料, 3 个测验
Video3 个视频
Cox proportional hazards assumption4分钟
Summary of Course2分钟
Reading7 个阅读材料
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
Quiz3 个练习
Assessing the proportionality assumption in practice5分钟
Testing the proportionality assumption with another variable15分钟
End-of-Module Assessment20分钟

讲师

Avatar

Alex Bottle

Reader in Medical Statistics
School of Public Health

关于 伦敦帝国学院

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....

关于 Statistical Analysis with R for Public Health 专项课程

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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