Survival Analysis in R for Public Health(으)로 돌아가기

4.5
별점
268개의 평가

## 강좌 소개

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....

## 최상위 리뷰

LA

2020년 7월 2일

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

VV

2019년 8월 26일

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

필터링 기준:

## Survival Analysis in R for Public Health의 62개 리뷰 중 51~62

교육 기관: Thiago Y

2021년 12월 6일

Great course for any person

교육 기관: Jaideepsinh d

2021년 1월 11일

good in detailed

교육 기관: Sara K

2020년 4월 17일

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.

교육 기관: NG, S L

2020년 9월 4일

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.

교육 기관: Shengyang L

2020년 2월 28일

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.

교육 기관: Jiasi H

2019년 12월 7일

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

교육 기관: Edward J

2021년 5월 23일

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

교육 기관: Jean-Philippe M

2021년 2월 2일

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

교육 기관: Yinhao W

2021년 9월 14일

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

교육 기관: Xinyu W

2020년 3월 10일

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

교육 기관: Ibrahim D K

2020년 4월 16일

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교육 기관: Jia L

2021년 11월 19일

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.