A Survival Mediation Model with Bayesian Model Averaging

TL;DR

This paper introduces a Bayesian mediation model with model averaging to analyze how tumor response mediates treatment effects on survival in cancer trials, providing robust inference even with model uncertainty.

Contribution

It develops a novel Bayesian framework combining mediation analysis and model averaging to better understand treatment effects on survival in oncology trials.

Findings

Applied to colorectal cancer trial data demonstrating the model's utility.

Provided posterior distributions for direct and mediated treatment effects.

Enhanced understanding of treatment pathways in cancer therapy.

Abstract

Determining the extent to which a patient is benefiting from cancer therapy is challenging. Criteria for quantifying the extent of "tumor response" observed within a few cycles of treatment have been established for various types of solid as well as hematologic malignancies. These measures comprise the primary endpoints of phase II trials. Regulatory approvals of new cancer therapies, however, are usually contingent upon the demonstration of superior overall survival with randomized evidence acquired with a phase III trial comparing the novel therapy to an appropriate standard of care treatment. With nearly two thirds of phase III oncology trials failing to achieve statistically significant results, researchers continue to refine and propose new surrogate endpoints. This article presents a Bayesian framework for studying relationships among treatment, patient subgroups, tumor response and survival. Combining classical components of mediation analysis with Bayesian model averaging (BMA), the methodology is robust to model mis-specification among various possible relationships among the observable entities. Posterior inference is demonstrated via application to a randomized controlled phase III trial in metastatic colorectal cancer. Moreover, the article details posterior predictive distributions of survival and statistical metrics for quantifying the extent of direct and indirect, or tumor response mediated, treatment effects.