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

TL;DR

This paper introduces a hybrid dose-escalation design for oncology phase I trials that combines non-informative and informative priors to improve dose selection when adding new doses mid-trial, with adaptive extensions for robustness.

Contribution

It proposes a novel hybrid model-assisted design with adaptive extensions for better dose insertion and MTD/OBD selection during ongoing trials.

Findings

Enhanced dose assignment accuracy in simulations

Robustness to skeleton misspecification demonstrated

Improved safety and efficacy data collection

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.