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Exploring Time-to-Event with Survival Analysis

Introduction of Survival Analysis and its application in Python

Survival analysis is a branch of statistics focused on analyzing the expected duration of time until one event occurs. It has been largely used in the healthcare sector, with the main use case being to understand the probability of one’s survival in medical trials.

This approach can also be applied to other domains and use cases with the objective of looking into the likelihood of a certain event at a given time. In this article, we will be looking into Survival Analysis concepts, techniques, and their application in Python.

Survival Analysis Concepts

In working on Survival Analysis, it is necessary to define an “event” and the “lifeline” or survival duration related to the event.

• Event: A thing that happens to the object of study. This needs to be unambiguous and binary, such as the death of a biological object. In more ambiguous domains like machinery failure, a clear definition is needed to identify the event (i.e. full failure, or productivity < X%).
• Lifeline / Survival duration: Time until the above event of interest occurs (or the end of observations).