Zurück zu Survival Analysis in R for Public Health

4.5

Sterne

154 Bewertungen

•

40 Bewertungen

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....

Jul 03, 2020

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

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.

Filtern nach:

von Todd D

•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.

von 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.

von 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.

von Kenil C

•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.

von Retham L

•May 11, 2020

This course can be improved by fixing mistakes (especially the ones on quizzes), and instructors need to be more active on the forums and help students with questions.

von Paco C

•Jul 22, 2020

Very shallow and uninformative. Nothing that cannot be learned with a 30 minutes read plus another 30 minutes looking at the survival package in R. Certainly doesn't merit the cost of subscribing to the specialization

von Merce G P

•May 25, 2020

Very nice course to get an introduction on survival analysis in R.

Well organized combination of theory and practice.

Also very nice it you have previous experience on survival analysis with other software like STATA.

Keep in mind that it is for a beginner level and basically covers kaplan-meier and cox, that's all.

von Eleanor H

•Jun 11, 2020

Great introduction to survival analysis, explaining key concepts in a simple and effective manner. The combination of videos, reading material and practical R sessions were a great variety and kept you engaged. Minimal prior knowledge required, and a great practical application. Would highly recommend this course!

von Nevin J

•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

von Dr. S P

•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.

von Lesaffre A

•Jul 03, 2020

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

von Xiyang S

•May 10, 2020

Thanks the course team especailly the instructor presenting great martirals, hope to see more related courses with more mathematics in it!

von 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.

von Assal h

•Aug 02, 2019

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

von Kaoma M M

•May 30, 2020

Very enjoyable course, and simple but effective application using R which I know very well in my practice

von Karina S

•Nov 12, 2019

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

von Sergio P

•Nov 07, 2019

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

von Faisal A

•Jul 22, 2019

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

von Anusha B

•Jun 15, 2020

Awesome course learned a lot from this entire series. Thank you!!!

von MOHAMMAD R W

•Dec 26, 2019

Take this course alongwith linear and logistic regression in R

von Junwen Z

•Mar 15, 2020

Very good introduction course for survival analysis in R

von Sidney d S P B

•Jul 05, 2020

Excelent! Professor Alex Bottle is superb!

von Ronpichai C

•May 25, 2020

Great course for survival analysis!!!!!

von Jin C

•Jul 31, 2020

Nice lecture by the excellent lecturer

- KI für alle
- Vorstellung von TensorFlow
- Neuronale Netzwerke und Deep Learning
- Algorithmen, Teil 1
- Algorithmen, Teil 2
- Maschinelles Lernen
- Maschinelles Lernen mit Python
- Maschinelles Lernen mittels Sas Viya
- R-Programmierung
- Einführung in die Programmierung mit Matlab
- Datenanalyse mit Python
- AWS-Grundlagen: Mit der Cloud vertraut werden
- Grundlagen der Google Cloud-Plattform
- Engineering für Site-Funktionssicherheit
- Englisch im Berufsleben
- Die Wissenschaft des Wohlbefindens
- Lernen lernen
- Finanzmärkte
- Hypothesenüberprüfung im öffentlichen Gesundheitswesen
- Grundlagen für Führungsstärke im Alltag

- Deep Learning
- Python für alle
- Data Science
- Angewandte Datenwissenschaft mit Python
- Geschäftsgründungen
- Architektur mit der Google Cloud-Plattform
- Datenengineering in der Google Cloud-Plattform
- Von Excel bis MySQL
- Erweiterte maschinelles Lernen
- Mathematik für maschinelles Lernen
- Selbstfahrende Autos
- Blockchain-Revolution für das Unternehmen
- Unternehmensanalytik
- Excel-Kenntnisse für Beruf
- Digitales Marketing
- Statistische Analyse mit R im öffentlichen Gesundheitswesen
- Grundlagen der Immunologie
- Anatomie
- Innovationsmanagement und Design Thinking
- Grundlagen positiver Psychologie