Survival Analysis Part 1 | What is Censoring?
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- Опубликовано: 10 дек 2024
- This video introduces Survival Analysis, and particularly focuses on explaining what censoring is in survival analysis. This video is the first in a series of videos covering survival analysis. The series contains videos that explain the concepts as well as vides that show implementation using the statistical software R.
These videos were put together quickly in response to the COVID-19 pandemic, and are being used as we transition classes to be online for the remainder of the year.
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This came after watching so many confusing and shitty articles,
your videos are the best!
Me too! :)
one of the best teacher on youtube. thank you for explaining this so well
Thank you so much. I learned more from this 9 min video than a whole lengthy lecture. I just love it when complicated things finally fall into place and you start seeing things clearly.
Good to hear :) I have a series of videos that covers 2 weeks of my course where we talk about survival analysis, in case you didn’t see those :) ruclips.net/p/PLqzoL9-eJTNDdnKvep_YHIwk2AMqHhuJ0
@@marinstatlectures Yes I have already started watching the videos in that playlist as I go through the lessons at school. Thank you very much!
Finally I got a useful series of survival analysis on your channel, Thanks a lot.
Thank you, your videos are clear and concise and I understand survival analysis much better from just watching this. You are great!
Thanks for the videos. I now understand a lot better about Survival Analysis than reading online articles. All my doubts and confusions are cleared away, and I have a much better idea on how to tackle my R project! :)
Very nicely done. Your explanation is very easy to follow. My understanding has considerably increased on this topic. THANK YOU!
Awesome lecture! Very easy to follow and easy to understand clearly! You are great!!!
This is my fault for not watching the first video first. I couldnt make sense of survival tables in R and now it all makes sense. Ligthbulb moment
Your video is very useful, simple explanatory method and brief lesson. Thanks a million for sharing.
THak you for all your videos. I just landed a brilliant opportunity. Thanks again.
Great to hear, congratulations!
Amazing explanation of the censoring! Thank you :)
Do you have any books to recommend on the topic?
Very clear and simpley explained
Thanks for an extremely informative video!
This made so much sense! Thank you for these videos!
Thank you for the wonderfully helpful video! One question, though. At 0:26, oh, really? Isn't the 'time' the X? The explanatory variable? I don't think it is Y. No?
No, time is the Y variable. Survival analysis is used to model time-till-event data. The outcome variable is the time until the event occurs (often death, but the event can be anything, not just death)
@@marinstatlectures Thank you very much : ) 👍
these are very great tutorials, thanks a lot!
Nice, clear explanation. Thanks
This is brilliant, thank you so much for this course! (I had to estimate the patience level of customers for my call center, fits perfectly 🙂)
The explanation is very helpful. Thank you very much!
Thank you professor. I am enjoying very much your channel.
the left and right censoring that you mention is that same as left and right truncation? (staggered entry is another name for it?)
This is brilliant.
super helpful, thanks so much!
If I have time to event data but no cases that are censored , do I need to do survival analyses ? Or can I do regular linear regression
Wonderful, thank you!
Very good lecture
sorry but i need to get this participation time correct - if follow a participant for 28 days - at day 0 is well - at day 14 is well - then at day 28 he is lost , I will simply consider his last check-up as the participation time =14 days ? or 28 days where I realize participant is maybe out of the areas
There are multiple ways to treat that. Because they were seen and healthy at 14 but lost at time 28, a common way to address this is to assume they were lost at the midpoint of those, and use 21 days. This is making an assumption though that they were equally likely to exit at any time in that interval. You could also choose to use 14 days, and thus would be being more conservative and likely underestimating survival a bit. It really depends on the assumption you want to make, but those are the two most common options (assuming you have no more knowledge on possible time lost to follow up). If it is only a few observations like this, it shouldn’t have much impact either way...if you have a high proportion of observations like this, your assumptions will have a greater impact.
Thank you sir you helped me
If the survival is censored, how should i record it in my data sheet? Should i record it as 0= Event did not happen? or record it with another code?
thank you for this informative video!
That’s right, 1 for event happened, 0 for didn’t happen/censored
Thank you for your help ❤️
Amazing, All the best
That was very nice
How did you get the confidence intervals from the Summary function???????
Hi Hunry Hu, I think you can't use just the Kaplan Meier method, because it is a non-parametric method. You should combine it with a Cox Proportional Hazards model. The Cox proportional Hazards is a Parametric method, so once you get a parametric method you will obtein confidence intervals. Excume my poor English, I am from Brazil. I hope I can help you.
@@thiagoluz1052 Thanks Thiago for the reply. I had mistakes in my R's Kaplan Meier function. I fixed it and now it included the confidence levels.
Thank you.
Great explanation. Thank you!
could you please give a few reasons on why a sample may be censored in a survival analysis.
The most common reasons are that the study ends and an individual is still alive, or they have stopped showing up and we no longer have access to them.
A censored observation means we know the survival time (T) is greater than the last time they were observed to be alive, by we don’t know how long they have survived, only that it is greater than the last time they were observed and alive
thank you 👏🏻
So nice :)
How can I make censoring 20% of the censored data?
What about informative censoring?
when he made the left censoring mistake i got scared for a bit....lol
well, there is a misleading concept. Left-Censoring IS NOT ABOUT the moment in which someone gets into the study! That is left truncation! Left-censoring is when the event occured before the observed value.
That’s correct. Left truncation is when the start point is to the left of the observation time interval. Left censoring is when the event occurred to the left of the observed time interval. Fortunately the focus of this video is right censoring and addressing that
These videos were created quite quickly when COVID really hit and our university transitioned to fully online classes over a weekend. The videos were recorded mostly unscripted, and I misspoke there. Unfortunately RUclips no longer allows annotations on top of an existing video to include the correction note.
@@marinstatlectures The serie is very good and it have helped me a lot in what i am doing right now. I just wanted to clarify that. But, everything is excelent! Thanks for sharing these concepts! Keep doing it
Thanks for the reply and clarification. I’m actually working on the first 9 lectures of this course, and will slowly be uploading all those soon, so check those out if you wanted more. They will cover linear, logistic, and poisson regression models.
can i trade you for my stats professor
You can if you transfer to UBC :)
mark zuckerberg