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

4.5

45개의 평가

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8개의 리뷰

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....

Aug 27, 2019

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

Jul 22, 2019

Very nice introductory course on survival analysis in R. Exercises were well designed.

필터링 기준:

교육 기관: Aboozar H

•Mar 06, 2019

This course does not discuss different types of survival model such as competitive event models. It only discusses very basic ideas such as the hazard function and the cox model which could be discussed in like 20 minutes. There are a lot of unnecessary discussion around multivariate regression and missing values that belong to a course on regression analysis and not survival analysis. The R code is a bit faulty and could be improved. Overall, I don't think this could be a good course on survival analysis.

교육 기관: Faisal A

•Jul 22, 2019

Very nice introductory course on survival analysis in R. Exercises were well designed.

교육 기관: Assal h

•Aug 02, 2019

Excellent course to learn about survival analysis, with very explicit explications of the application of the models on R

교육 기관: Victoria

•Aug 27, 2019

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

교육 기관: Sergio P

•Nov 07, 2019

Excellent course. Definitely a MUST DO if you would like to learn statistics in RStudio.

교육 기관: Karina S

•Nov 12, 2019

Great! It's very interesting! Thank you. I would like to find out about prediction based on Cox model

교육 기관: Basilio G P

•May 13, 2019

High-quality, thoroughly-designed, hands-on, introductory course.

교육 기관: Amir A H

•May 16, 2019

There are few videos and too much text. The exercises have not been well prepared and some outcomes and results have not been discussed, in particular for different types of residuals in the last week.