# 学生对 伦敦帝国学院 提供的 Survival Analysis in R for Public Health 的评价和反馈

4.4
82 个评分
20 条评论

## 课程概述

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....

## 热门审阅

##### VV

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.

##### SP

Nov 07, 2019

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

## 1 - Survival Analysis in R for Public Health 的 20 个评论（共 20 个）

Nov 25, 2019

Overall, the series on Stats in Public Health was worthwhile, well-constructed, and very informative. This last course (survival analysis) was equally informative, but desperately needs attention to the course presentation. The video transcripts were still raw (there needs to be an easy way for students like me who created cleaned video transcripts to upload them), two of the Week 4 quizzes would not accept the correct answers generated by the current software release (answer key needs to be updated), and the course itself needs someone to spend a few hours looking for bugs, typos, and doing polishing. The content is great but the presentation undermines it. Still, I would recommend the series, the course, and the instructors to other students.

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.

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.

Feb 08, 2020

I expected the course to be more in-depth about the theory about survival analysis, but it only covers the very basics and the exercises are simply copying-and-pasting some R statements and getting some p-values.

Feb 01, 2020

Brilliant course from the Imperial team taking you through survival analysis using R. Practical, applicable and well explained. I finally understood a topic that I have had trouble with. It builds upon foudnations and beautfilly builds as u progress. it uses assessments really well to test knowledge

Jan 04, 2020

The course has been designed to cater to the requirement of budding public health professionals who want to enhance their skills beyond basic of epidemiology and biostatistics and gain a competitive edge.

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.

Aug 02, 2019

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

Nov 12, 2019

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

Nov 07, 2019

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

Jul 22, 2019

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

Dec 26, 2019

Take this course alongwith linear and logistic regression in R

Mar 15, 2020

Very good introduction course for survival analysis in R

Mar 04, 2020

Excellent course!

Nov 22, 2019

The final quiz is a little bit confusing ,pls provide detailed feedback on it so we can learn further even we did not pass it.

Mar 17, 2020

A fantasic intro to learn survival analysis where the time to the outcome is important

May 13, 2019

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

Feb 29, 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.

Dec 07, 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

Mar 10, 2020

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