Este curso faz parte do Programa de cursos integrados Statistical Analysis with R for Public Health

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Programa de cursos integrados Statistical Analysis with R for Public Health

Imperial College London

Informações sobre o curso

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.

Comece imediatamente e aprenda em seu próprio cronograma.

Redefinir os prazos de acordo com sua programação.

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

Sugerido: 3-5 hours/week...

Legendas: Inglês

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

Comece imediatamente e aprenda em seu próprio cronograma.

Redefinir os prazos de acordo com sua programação.

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

Sugerido: 3-5 hours/week...

Legendas: Inglês

Semana

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 vídeos (total de (Total 16 mín.) min), 11 leituras, 3 testes

What is Survival Analysis?4min

The KM plot and Log-rank test4min

What is Heart Failure and How to run a KM plot in R4min

About Imperial College & the team10min

How to be successful in this course10min

Grading policy10min

Data set and glossary10min

Additional Readings10min

Life tables20min

Feedback: Life Tables10min

The Course Data Set20min

Feedback: Running a KM plot and log-rank test3min

Practice in R: Run another KM Plot and log-rank test10min

Feedback: Running another KM plot and log-rank test10min

Survival Analysis Variables30min

Life tables30min

Practice in R: Running a KM plot and log-rank test20min

Semana

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 vídeos (total de (Total 18 mín.) min), 4 leituras, 2 testes

Hazard Function and Risk Set20min

Practice in R: Simple Cox Model30min

Feedback: Simple Cox Model10min

Further Reading20min

Hazard function and Ratio5min

Simple Cox Model15min

Semana

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 vídeo (total de (Total 6 mín.) min), 7 leituras, 1 teste

Introduction to Running Descriptives10min

Practice in R: Getting to know your data30min

Feedback: Getting to know your data10min

How to run multiple Cox model in R20min

Introduction to Non-convergence10min

Practice: Fixing the problem of non-convergence10min

Feedback on fixing a non-converging model15min

Multiple Cox Model10min

Semana

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 vídeos (total de (Total 11 mín.) min), 7 leituras, 3 testes

Checking the proportionality assumption10min

Feedback on Practice Quiz10min

What to do if the proportionality assumption is not met20min

How to choose predictors for a regression model20min

Practice in R: Running a Multiple Cox Model

Results of the exercise on model selection and backwards elimination10min

Final Code10min

Assessing the proportionality assumption in practice5min

Testing the proportionality assumption with another variable15min

End-of-Module Assessment20min

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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