Survival Analysis in Python

There are two objectives for this course. Regression and Classification models are studied widely but in terms of clinical contexts such as clinical trials and reliability analysis Survival Analysis concepts are widely used. This course aims to shed light on what Survival Analysis is and the areas in which Survival Analysis is widely applied to.

You will learn Python packages to conduct Survival Analysis and the applications of survival analysis

 

Python packages to conduct Survival Analysis and the applications of survival analysis

In this course, we will be looking at:

1. The basics of survival analysis concepts such as what is survival analysis and how it is different from traditional regression analysis.

2. What is Censoring and what types of censoring?

3. What do Survival, Cumulative, Hazard, and Cumulative Hazard functions mean?

4. Survival Analysis Metrics: Concordance-Index and Integrated Brier Score Basics of Conditional Probability

5. Survival Analysis Algorithms:

• Kaplan-Meier Estimator
• Nelson-Aalen Estimator
• Cox-Proportional Hazards Model
• Random Survival Forest
• DeepSurv and DeepHit models