Reduction Techniques for Survival Analysis

TL;DR

This paper reviews reduction techniques that transform survival analysis into standard regression or classification tasks, enabling the use of common machine learning tools without custom models.

Contribution

It provides an overview, implementation, and benchmark comparison of various reduction techniques for survival analysis, highlighting their strengths and weaknesses.

Findings

Reduction techniques enable applying standard ML tools to survival data.

Benchmark results compare the predictive performance of different reductions.

The paper offers practical implementations within existing ML workflows.

Abstract

In this work, we discuss what we refer to as reduction techniques for survival analysis, that is, techniques that "reduce" a survival task to a more common regression or classification task, without ignoring the specifics of survival data. Such techniques particularly facilitate machine learning-based survival analysis, as they allow for applying standard tools from machine and deep learning to many survival tasks without requiring custom learners. We provide an overview of different reduction techniques and discuss their respective strengths and weaknesses. We also provide a principled implementation of some of these reductions, such that they are directly available within standard machine learning workflows. We illustrate each reduction using dedicated examples and perform a benchmark analysis that compares their predictive performance to established machine learning methods for survival analysis.