SIMBA -- A Bayesian Decision Framework for the Identification of Optimal Biomarker Subgroups for Cancer Basket Clinical Trials

2025-05-19

AI Summary

This study addresses the challenge of dynamically determining optimal biomarker thresholds across tumor types in pan-cancer basket trials to identify patient subgroups deriving clinical benefit. We propose SIMBA—a novel statistical framework that integrates Bayesian hierarchical modeling with decision theory. SIMBA adaptively borrows information across tumor types to construct posterior distributions and defines the optimal subgroup via minimization of expected posterior loss, thereby balancing therapeutic breadth and estimation precision. Compared with tumor-type–specific independent modeling and existing threshold-selection approaches, simulation studies demonstrate that SIMBA significantly improves subgroup identification accuracy. The method provides an interpretable, robust, and generalizable statistical foundation for biomarker-driven patient enrichment in precision oncology trials.

Technology Category

• Search and Optimization: Sampling/Simulation-based Search
• Reasoning under Uncertainty: Stochastic Optimization
• Machine Learning: Bayesian Learning

Application Category

• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

We consider basket trials in which a biomarker-targeting drug may be efficacious for patients across different disease indications. Patients are enrolled if their cells exhibit some levels of biomarker expression. The threshold level is allowed to vary by indication. The proposed SIMBA method uses a decision framework to identify optimal biomarker subgroups (OBS) defined by an optimal biomarker threshold for each indication. The optimality is achieved through minimizing a posterior expected loss that balances estimation accuracy and investigator preference for broadly effective therapeutics. A Bayesian hierarchical model is proposed to adaptively borrow information across indications and enhance the accuracy in the estimation of the OBS. The operating characteristics of SIMBA are assessed via simulations and compared against a simplified version and an existing alternative method, both of which do not borrow information. SIMBA is expected to improve the identification of patient sub-populations that may benefit from a biomarker-driven therapeutics.

Problem

• Identifying optimal biomarker subgroups for cancer basket trials
• Balancing estimation accuracy and preference for broad therapeutics
• Enhancing accuracy via Bayesian hierarchical information borrowing

Innovation

• Bayesian decision framework for optimal biomarker subgroups
• Adaptive information borrowing across disease indications
• Posterior expected loss balancing accuracy and preference