Nonparametric Bayesian Multi-Treatment Mixture Cure Survival Model with Application in Pediatric Oncology

TL;DR

This paper introduces a flexible nonparametric Bayesian model for estimating treatment effects in pediatric oncology survival data, effectively handling heterogeneity, cure fractions, and shared treatment mechanisms.

Contribution

It develops a novel covariate-dependent Bayesian mixture cure model that captures treatment sharing and individual survival, with efficient inference via MCMC, advancing personalized treatment analysis.

Findings

Demonstrates robustness and superiority through simulations.

Identifies meaningful survival differences in pediatric trials.

Links treatment effects to covariate profiles.

Abstract

Heterogeneous treatment effect estimation is critical in oncology, particularly in multi-arm trials with overlapping therapeutic components and long-term survivors. These shared mechanisms pose a central challenge to identifying causal effects in precision medicine. We propose a novel covariate-dependent nonparametric Bayesian multi-treatment cure survival model that jointly accounts for common structures among treatments and cure fractions. Through latent link functions, our model leverages sharing among treatments through a flexible modeling approach, enabling individualized survival inference. We adopt a Bayesian route for inference and implement an efficient MCMC algorithm for approximating the posterior. Simulation studies demonstrate the method's robustness and superiority in various specification scenarios. Finally, application to the AALL0434 trial reveals clinically meaningful differences in survival across methotrexate-based regimens and their associations with different covariates, underscoring its practical utility for learning treatment effects in real-world pediatric oncology data.