Bayesian MultiState Mediation Model Elucidates the Impact of Treatment Response on Oncology Endpoint

Music City Center 

Chemoradiation for solid tumors in the thorax region targets rapidly dividing cells, including cancer cells but also immune population. Severe radiation-induced immunosuppression impairs effective immune responses against pathogens and cancer recurrence.
Unraveling the complex relationships between treatment, intermediate endpoints (TTP/PFS/DFS), and survival is crucial to translating scientific advances into therapeutic strategies. We developed a novel Bayesian multi-state mediation modeling framework to evaluate direct and indirect treatment effects on survival, which (1) explicitly incorporates intermediate time-to-failure outcomes observed post-treatment response, which are typically neglected in conventional survival analyses; (2) leverages Bayesian estimation and variable selection techniques to enhance model reliability and address uncertainty in parameters.
The method was applied to a study of esophageal cancer patients receiving photon (IMRT) vs proton (PBT) therapy to elucidate the impact of severe lymphopenia. Survival benefit in PBT group was shown to be attributable (mediated proportion 35%) to reduced immunosuppression (22.0% vs. 42.7%, respectively; P < 0.001).

Bayesian

Multistate model

Oncology

Clinical trials

Surrogate markers

Mediation analysis 

Biopharmaceutical Section