Individualized Treatment Effects in Advanced Prostate Cancer: A Causal-Survival Modeling Approach to Risk-Guided Therapy

TL;DR

This study demonstrates a method to estimate individualized treatment effects in advanced prostate cancer using survival models with interaction terms, revealing significant heterogeneity in treatment benefits based on patient age and tumor size.

Contribution

It introduces a causal-survival modeling approach with interaction terms and robust inference for estimating patient-specific treatment effects in oncology trials.

Findings

Younger patients benefit more from high-dose DES.

Patients with larger tumors experience greater treatment benefit.

Robust uncertainty quantification methods were validated.

Abstract

We conducted a proof-of-concept evaluation of individualized treatment effect (ITE) estimation using survival data from a randomized trial of 475 men with advanced prostate cancer treated with high- versus low-dose diethylstilbestrol (DES). A Weibull accelerated failure time (AFT) model with interaction terms for treatment-by-age and treatment-by-log tumor size was used to capture subgroup-specific treatment effects. The estimated main effect of high-dose DES indicated a time ratio of 0.582 (95% CI: [0.306, 1.110]), reflecting reduced survival at the reference levels of age and tumor size. However, interaction-adjusted ITEs revealed marked effect modification: younger patients (e.g., age 50 years) had over fourfold expected survival gains (time ratio 4.09), whereas older patients (e.g., age 80 years) experienced reduced benefit (time ratio 0.71). Similarly, patients with larger tumors (log size $\sim$4.25, $\sim$70 $cm^2$) derived a stronger benefit (time ratio 1.89) than those with smaller tumors. To evaluate the reliability of these individualized estimates, both the delta method and bootstrap resampling were applied for uncertainty quantification, producing closely aligned intervals across the risk spectrum. This analysis illustrates how parametric survival models with clinically motivated interactions and robust inference procedures can yield interpretable patient-level treatment effect estimates, even in moderately sized oncology trials.