A versatile and powerful framework to identify patient subgroups using dense random survival forests

Music City Center 

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 utilizing very dense random survival forests (up to 100,000 trees). 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.

subgroup analysis

survival analysis

machine learning

Phase III clinical trial

random forest

unsupervised learning 

Biometrics Section