Survival Analysis in Python [Time to event analysis]

Survival analysis is a branch of statistics for analysing the expected duration of time until one or more events occur. This topic is called reliability analysis in engineering, duration analysis or duration modelling or time to event analysis in economics, and event history analysis in sociology. Survival analysis attempts to answer the following questions:
* Proportion of population surviving up to a given time
* Rate at which they are dying
* How predictors could explain the time to event analysis
There are plenty of application scenarios, where we could utilize survival analysis. Some are described as follows:
* After diagnose with cancer when a patent would die
* Hospital readmission duration after a major heart surgery
* When would Tesla car battery permanently die/fail
* Waiting time of pedestrian after red (do-not walk) phase arrival at intersection
* After seeing ads when a person would purchase the product
* When an employee would leave a company (churn)
Reading resources
Blog1: sphweb.bumc.bu...
Blog2: boostedml.com/...
Survival analysis concept videos
MarinStatsLectures-R Programming & Statistics
• Survival Analysis Part...
Zedstatistics
• Introduction to Surviv...

[@drcemdede]

Thank you very much for this video and the materials !
The combination of dataset + notebooks is an excellent source of materials.

[@johnspivack6520]

It is very good material, well organized. Explanations are fine, too. The problem is that the presenter talks much too fast and has a thick accent. He needs to slow down and pronounce his words clearly and carefully. That would make this a great video rather than just an okay one.

[@aprilchen8042]

Good instruction video. Thanks. Hope to see more videos on Python.

[@josephbates1768]

Survival Analysis in SAS is superior

[@kandiahchandrakumaran8521]

Excellent video. Easy to understand. I am novice to Python / ML but youo video gave me a lot of confidence.
Have you done model prediction, especially Random Survival Forest - not Random Forest, because of censored data? If so where can I find it?
Also, are you able to create a video for python to generate nomogram for competing variables in survival.
Many thanks

[@ZVahedi]

Hey! thank you for the the walkthrough. I have two questions, in the COX model, why did not you concatenate the ecog_0 as well? The next question is if I ave multiple categorical column, should I do the same as you have done for ecog?

[@niteshkavathankar1300]

I had a doubt what can we use to predict the event survival function or Cumulative hazard rate in case of COX proportional regression model

[@ddrikee2546]

Do you have relevant experience in machine learning survival analysis? For example xgbse , scikit survival and pycox package in python. I don't know how the data format is handled in this case, also time dependent ROC, calibration and DCA.

[@user-bl8hi7je1z]

Thanks for sharing this ,could you plz shar the code file with data base

[@TheResearchersGuide]

The code and data files are available in my Github repository. Kindly visit the following link:
github.com/rahul-raoniar/The_Researchers_Guide/tree/master/Survival%20Analysis

[@user-bl8hi7je1z]

@@TheResearchersGuide thanks alot🙏

[@anonymousoul3779]

@@TheResearchersGuide 404 - page not found showing
please can you share again

[@learnnend8022]

Can we use this to know polis survival in insurance industry

[@kaiit0]

@@TheResearchersGuide why did you deleted it lol