Survival Analysis Part 12 | Checking Cox PH Model Assumptions in R with RStudio
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- เผยแพร่เมื่อ 3 ม.ค. 2025
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Content Creator: Mike Marin (B.Sc., MSc.) Senior Instructor at UBC.
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These videos are created by #marinstatslectures to support some statistics courses at the University of British Columbia (UBC) (#IntroductoryStatistics and #RVideoTutorials ), although we make all videos available to the everyone everywhere for free.
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Wonderful refresher. Clearly explained, helpful tips. Great work!
Excellent. Looking forward to the script when you upload it.
This course answered some questions I had after working through Kleinbaum‘s Survival Analysis: A self-learning Text. Thank you 🙏🏻
YOU ARE A GODSEND.
This lecture is so helpful thanks a lot!
I find your classes really interesting. I wonder if there will be more videos explaining the fitting of survival with the parametric models (exponential and Weibull). I would really love to see them. Thank you very much in advance. I really appreciate your work!
Your videos are very interesting! You explain concepts and their procedures very nicely and in a way that is easy to understand for anyone. I would love to see your video/s on mixed cox model and how to check proportional hazard assumptions in this model. Thank you so much! Keep the good work up!
Thank you for this course. Great explanations and very helpful!
Thank you for these explanations! Are the codes available for everyone to download?
You can check for linearity using ggcoxfunctional(surv(time, status) ~ age+log(age)+ sqrt(age), data=lung) but why do we add the option log(age) and sqrt(age)? Is it checking if tgd transformations are linear? (This is from the dataset lung from R)
Hello, thank you so much for creating these videos. Where can we access the R scripts from?
I wonder that too!
Thank you very much for your explicit and concise videos on survival models. Could you please share explanations about Weibull distribution model , its assumption, interpretation of results and key results one would present from the model? And could you suggest books to read about these models?
Thank you.
Thank you for these lectures, they are very useful. Did you share the R code anywhere?
Thanks, Marin. Could you please upload the rscript?
It's really useful, thank you very much!
How to deal with cases which do not meet Cox PH Model Assumptions in R? Thanks
Respected sir
Could you suggest standard reference books for learning R in detail.
I am following all of your lectures, it is very helpful all of my university friends following this lecture series.
Thank you
If the concordance index is less for example c-index 0.5. How we can improve this concordance index in R language? If we used optimisation method then which parameters we need to used in Coxph function. Thank you.
Hi, are next set of videos again coming on Wednesday or sooner?
Yes. Those correspond to the last lecture for the course, and are a course overview.
Once things settle down, I hope to record lectures for the first ~9 weeks of the course, although it will take some time to get to making those
@@marinstatlectures Could you suggest any reference for time varying variables and cox model please? I have a journal club and the variable transplant is time-dependent and therefore instead of HR they reported hazard factor, I wish to learn more about this. Thanks
great great great, very good. I just can say that.
waiting for part 13
Did you find it ?
why you always separate categorical and continuous variables? that is misleading.
I’m sorry, I do not understand what you are trying to say in your comment. If you can clarify what you mean I may be able to help you out