A Bayesian model-free dose-finding approach in Phase II clinical trials with the flexibility of historical borrowing

2025-09-28

AI Summary

Phase II dose selection is often hindered by model misspecification risk—e.g., MCP-Mod requires prespecifying candidate dose–response models—thereby compromising Phase III success. Method: We propose MAP-curvature, a Bayesian framework that avoids prespecifying parametric models. It employs curvature-penalized priors for flexible nonlinear modeling and embeds a sigmoid Emax structure to better capture pharmacological S-shaped responses. The method innovatively integrates model-agnostic flexibility with Bayesian hierarchical modeling, enabling robust borrowing from historical data. Posterior inference is performed via MCMC, balancing curve smoothness and biological plausibility. Contribution/Results: Simulation studies demonstrate that MAP-curvature significantly outperforms MCP-Mod and linear models under S-shaped scenarios, yielding higher statistical power, improved detection of dose–response signals, and more accurate estimation of the minimum effective dose—thereby enhancing the reliability of Phase II-to-Phase III translation.

Technology Category

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

Application Category

• User Modeling, Personalization and Recommendation: Explainable and interpretable methods for personalization
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

Accurate dose selection in Phase II trials is critical to the success of subsequent Phase III trials, but suboptimal choices remain a leading cause of trial failure and regulatory rejection. Although MCP-Mod is widely adopted and endorsed by regulatory agencies, it requires prespecification of candidate models and is highly sensitive to model misspecification. To address these challenges, we introduce MAP-curvature, a general model-free framework for dose-response modelling that penalises the total curvature of the dose-response curve through a prior. Within this framework, LiMAP-curvature arises as the linear special case, whereas SEMAP-curvature, the focus of this work, employs the sigmoid Emax model, providing greater flexibility to capture nonlinear pharmacological patterns. Through extensive simulations, we show that SEMAP-curvature generally outperforms LiMAP-curvature and MCP-Mod in detecting the dose-response signal, estimating the dose-response curve and identifying the minimum effective dose, with particularly significant improvements under concave downward shapes resembling the sigmoid Emax model. Although SEMAP-curvature exhibits slightly greater variability, it remains robust in accuracy and reliability. We further extend MAP-curvature by integrating it with the Bayesian hierarchical model to enable flexible borrowing of historical data, which improves power and precision, particularly when dose levels overlap across studies. These results highlight MAP-curvature, and in particular SEMAP-curvature with historical borrowing, as a robust and efficient framework for dose selection in early-phase clinical trials.

Problem

• Addressing model misspecification in Phase II dose-response trials
• Developing flexible historical data borrowing for dose selection
• Improving accuracy in minimum effective dose identification

Innovation

• MAP-curvature uses Bayesian model-free dose-response framework
• SEMAP-curvature employs sigmoid Emax model for nonlinear patterns
• Integrates Bayesian hierarchical model for historical data borrowing