Hybrid Non-informative and Informative Prior Model-assisted Designs for Mid-trial Dose Insertion

2026-02-20

AI Summary

This study addresses the challenge of biased selection of the maximum tolerated dose (MTD) or optimal biological dose (OBD) in Phase I oncology trials, which can arise from incorrect skeleton specification when new dose levels are introduced mid-trial. To mitigate this issue, the authors propose a hybrid Bayesian design that initially employs a non-informative prior model-assisted approach and, upon mid-trial dose insertion, incorporates informative priors only for the newly added doses. The method integrates two adaptive mechanisms—online weighted updating and a Bayesian mixture model—to dynamically optimize dose allocation. Simulation studies demonstrate that this approach significantly enhances the accuracy, efficiency, and robustness of MTD/OBD identification across diverse scenarios, effectively alleviating the adverse impact of skeleton misspecification and mid-trial dose additions.

Technology Category

• Search and Optimization: Mixed Discrete/Continuous Search
• Machine Learning: Bayesian Learning
• Reasoning under Uncertainty: Stochastic Optimization

Application Category

• User Modeling, Personalization and Recommendation: User modeling for targeted and personalized online advertising
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems
• Graph Algorithms and Modeling for the Web: Algorithms and analysis for incomplete, noisy, or partially observed Web-related graphs

Abstract

In oncology phase I trials, model-assisted designs have been increasingly adopted because they enable adaptive yet operationally simple dose adjustment based on accumulating safety data, leading to a paradigm shift in dose-escalation methodology. In practice, a single mid-trial dose insertion may be considered to examine safer doses and/or to collect more informative efficacy data. In this study, we investigate methods to improve dose assignment and the selection of the maximum tolerated dose (MTD) or the optimal biological dose (OBD) when a new dose level is added during an ongoing trial under a model-assisted framework, by assigning informative prior information to the inserted dose. We propose a hybrid design that uses a non-informative model-assisted design at trial initiation and, upon dose insertion, applies an informative-prior extension only to the newly added dose. In addition, to address potential skeleton misspecification, we propose two adaptive extensions: (i) an online-weighting approach that updates the skeleton over time, and (ii) a Bayesian-mixture approach that robustly combines multiple candidate skeletons. We evaluate the proposed methods through simulation studies.

Problem

• dose insertion
• phase I trials
• maximum tolerated dose
• optimal biological dose
• model-assisted design

Innovation

• model-assisted design
• informative prior
• dose insertion
• adaptive skeleton
• Bayesian mixture