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Survival Analysis with Python Tutorial — How, What, When, and Why
Diving into survival analysis with Python — a statistical branch used to predict and calculate the expected duration of time for one or more significant events to occur.
Author(s): Pratik Shukla
Last updated, January 8, 2021
This article covers an extensive review with step-by-step explanations and code for how to perform statistical survival analysis used to investigate the time some event takes to occur, such as patient survival during the COVID-19 pandemic, the time to failure of engineering products, or even the time to closing a sale after an initial customer contact.
This tutorial’s code is available on Github and its full implementation on Google Colab.
Table of Contents:
- Survival Analysis Basics
- Kaplan-Meier fitter Theory with an Example.
- Nelson-Aalen fitter Theory with an Example.
- Kaplan-Meier fitter Based on Different Groups.
- Log-Rank-Test with an Example.
- Cox-Regression with an Example.
- Resources.
📚 Check out our Monte Carlo Simulation Tutorial with Python 📚
1. Survival Analysis Basics:
Survival analysis is a set of statistical approaches used to determine the time it takes for an event of interest to occur. We use survival analysis to study the time until some event of interest occurs. Time is usually measured in years, months, weeks, days, and other time measuring units. The event of interest could be anything of interest. It could be an actual death, a birth, a retirement, along with others.
