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In this video I cover survival analysis. Specifically what it is, and why it's useful when the time until an event is important and when you have "censored" data. I talk about what censored data is and provide definitions of the survival and hazard functions. This is illustrated visually by showing a Kaplan-Meier curve as well as the idea behind the logrank test. We close with modeling. There's a lot of different survival models, such as survival trees and survival forests. For parametric versions there's accelerated failure time models. I show an example of a Cox proportional hazards model and explain what exactly the (very important) proportional hazards assumption means.
Real world example: https://web.cortland.edu/matresearch/
Censored data example: https://blog.minitab.com/en/michelle-...
Kaplan-Meier Curve example: https://sphweb.bumc.bu.edu/otlt/mph-m...
Logrank test example: https://sphweb.bumc.bu.edu/otlt/MPH-M...
Cox model example: https://sphweb.bumc.bu.edu/otlt/mph-m...
Packages for survival analysis:
Python: https://github.com/CamDavidsonPilon/l...
R: https://github.com/therneau/survival