Unsupervised dense random survival forests identify interpretable patient profiles with heterogeneous treatment benefit

TL;DR

This paper introduces an unsupervised machine learning method using dense random survival forests to identify patient subgroups with heterogeneous treatment benefits in cancer, enhancing precision oncology.

Contribution

It presents a novel, robust, and interpretable approach with a new splitting rule to detect treatment-effect heterogeneity in clinical trial data.

Findings

Successfully detects patient subgroups with different treatment responses

Maintains a low Type I error rate of 1% in simulations

Validates heterogeneity in real clinical trial datasets

Abstract

Precision oncology aims to prescribe the optimal cancer treatment to the right patients, maximizing therapeutic benefits. However, identifying patient subgroups that may benefit more from experimental cancer treatments based on randomized clinical trials presents a significant analytical challenge. To address this, we introduce a novel unsupervised machine learning approach based on very dense random survival forests (up to 100,000 trees), equipped with a new splitting rule that explicitly targets treatment-effect heterogeneity. This method is robust, interpretable, and effectively identifies responsive subgroups. Extensive simulations confirm its ability to detect heterogeneous patient responses and distinguish between datasets with and without heterogeneity, while maintaining a stringent Type I error rate of 1%. We further validate its performance using Phase III randomized clinical trial datasets, demonstrating significant patient heterogeneity in treatment response based on baseline characteristics.