CAST: Time-Varying Treatment Effects with Application to Chemotherapy and Radiotherapy on Head and Neck Squamous Cell Carcinoma
Journal:
arXiv
Published Date:
May 9, 2025
Abstract
Causal machine learning (CML) enables individualized estimation of treatment
effects, offering critical advantages over traditional correlation-based
methods. However, existing approaches for medical survival data with censoring
such as causal survival forests estimate effects at fixed time points, limiting
their ability to capture dynamic changes over time. We introduce Causal
Analysis for Survival Trajectories (CAST), a novel framework that models
treatment effects as continuous functions of time following treatment. By
combining parametric and non-parametric methods, CAST overcomes the limitations
of discrete time-point analysis to estimate continuous effect trajectories.
Using the RADCURE dataset [1] of 2,651 patients with head and neck squamous
cell carcinoma (HNSCC) as a clinically relevant example, CAST models how
chemotherapy and radiotherapy effects evolve over time at the population and
individual levels. By capturing the temporal dynamics of treatment response,
CAST reveals how treatment effects rise, peak, and decline over the follow-up
period, helping clinicians determine when and for whom treatment benefits are
maximized. This framework advances the application of CML to personalized care
in HNSCC and other life-threatening medical conditions. Source code/data
available at: https://github.com/CAST-FW/HNSCC
