A Constrained Hierarchical Bayesian Model Considering Latent Biomarker Subgroups for Time-to-Event Endpoints in Randomized Phase II Trials

Music City Center 

In randomized Phase III oncology trials, long-term time-to-event endpoints are crucial for assessing treatment benefits. However, Phase II trials often rely on short-term binary tumor response as a surrogate endpoint, which can lead to high failure rates in Phase III trials, as tumor response may not accurately reflect survival benefits. Additionally, many oncology trials collect biomarker data to identify participants more likely to respond to experimental treatments, highlighting the need for biomarker-based designs to enrich trial populations.
This presentation introduces a constrained hierarchical Bayesian model considering latent biomarker subgroups (CHBM-LS) for long-term time-to-event endpoints in Phase II randomized trials. CHBM-LS aggregates biomarker populations into latent subgroups and addresses treatment effect heterogeneity across biomarker levels. We compare our design with other approaches, demonstrating that CHBM-LS improves the accuracy of hazard ratio estimates and enhances the power to detect true effects while maintaining control over the Type I error rate.

biomarker

heterogeneity

latent subgroup

constrained hierarchical Bayesian model

Bayesian adaptive design