Goodness of Fit of a Survival Regression Model

Goodness of Fit of a Survival Regression Model

Cox’s Proportional Hazard & Lifelines Python Package

Introduction

Survival analysis is a branch of statistics for analyzing the expected duration of time until one or more events happen, such as customer churn in a marketing perspective, a death in biological organisms, failure in mechanical systems. Generally, it involves the modeling of time to event data; in this context, death/failure/churn is taken as an “event” in the survival analysis literature — usually, only a single event occurs for each subject, after which the customer, organism or mechanism is dead or broken or churned[1]. Survival analysis attempts to answer specific questions, such as:

  • What proportion of a population will survive beyond a particular timeframe?Of those that manage to survive, at what rate will they fail or die?Can multiple causes of failure or death can be taken into account?How do particular characteristics or circumstances increase or decrease the probability of survival?

This article will give us a fundamental overview of Cox’s proportional hazard model, hazard & hazard ratio and ‘Lifelines’ python package. It then details the key focus, i.e. measuring the goodness of fit of the survival regression model.

machine-learning python

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