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Comentários e feedback de alunos de Survival Analysis in R for Public Health da instituição Imperial College London

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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....

Melhores avaliações

LA

2 de jul de 2020

Great course superb support and very clear professor. This course is a good motivator to continue to explore public health and statistics.

VV

26 de ago de 2019

Good and practical introduction to survival analysis. I liked the emphasis on how to deal with practical data sets and data problems.

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51 — 62 de 62 Avaliações para o Survival Analysis in R for Public Health

por Thiago Y

6 de dez de 2021

Great course for any person

por Jaideepsinh d

11 de jan de 2021

good in detailed

por Sara K

17 de abr de 2020

It made learning very frustrating in every sense. Grading system has obviously some errors and nobody provides answers on Discussion forum. Final questions are formed in a way that was quite confusing to me and I never had that problem before also in much harder courses. Important things are not well explained including the mathematics behind. These is a lot of space for improving this course to make it better which is a pity because the course has some good moments as well.

por NG, S L

4 de set de 2020

The transcript is poorly made so I could not save notes without translating the transcript. There are bugs in quizzes (wrong model answer) too. Otherwise, I have gain much knowledge about Cox's regression.

por Shengyang L

28 de fev de 2020

Got some setting error and not yet be fixed in week 4. The incorrect setting or answer set prevent the student from passing the quiz and proceed the course.

por Jiasi H

7 de dez de 2019

It is a nice course! However, the video transcripts are very problematic. Since I like taking notes from transcripts, it creates some inconvenience for me

por Edward J

23 de mai de 2021

Really enjoyed the course and the instructor was very engaging. However, the wording in some of the assessments is woeful and extremely frustrating.

por Jean-Philippe M

2 de fev de 2021

Great course overall but this last one, tests and course explanations were not aligned if my opinion.

por Yinhao W

14 de set de 2021

The feedback on the quizzes is extremely inadequate. Very difficult to understand your mistakes.

por Xinyu W

10 de mar de 2020

not a lot of technical details are explained in this course thus a bit hard to understand

por Ibrahim D K

16 de abr de 2020

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por Jia L

19 de nov de 2021

Rarely any clear explanation, no formulas at all. Just making conclusions on statistics but not telling why. Truly doesn't worth the time. I would rather spend much more time reading a book rather than going over such reckless and seemingly good papers and projects.