A Comparative Evaluation of Bayesian Model-Assisted Two-Stage Designs for Phase I/II Clinical Trials

TL;DR

This paper compares fifteen Bayesian model-assisted two-stage designs for Phase I/II clinical trials, evaluating their performance through extensive simulations to recommend the most effective combination for dose-finding and optimization.

Contribution

It provides a comprehensive comparison of multiple Bayesian two-stage designs and identifies the TITE-BOIN12 + TOP combination as optimal for Phase I/II trials.

Findings

TITE-BOIN12 + TOP performs best across scenarios.

Bayesian model-assisted designs improve dose selection accuracy.

Simulation results support recommended design combination.

Abstract

The primary goal of a two-stage Phase I/II trial is to identify the optimal dose for the following large-scale Phase III trial. Recently, Phase I dose-finding designs have shifted from identifying the maximum tolerated dose (MTD) to the optimal biological dose (OBD). Typically, several doses are selected as recommended Phase II doses (RP2D) for further evaluation. In Phase II dose optimization trials, each RP2D is evaluated independently to determine its "go/no-go" decision. The optimal RP2D is then chosen from the remaining RP2Ds as the recommended Phase III dose (RP3D). The effectiveness of both dose-finding and dose optimization designs at two stages impacts RP3D selection. This paper reviews and compares fifteen Bayesian model-assisted two-stage designs, combining five Phase I dose-finding designs (BOIN, TITE-BOIN, BF-BOIN, BOIN12, and TITE-BOIN12) with three Phase II dose optimization designs (TS, BOP2, and TOP). We conduct extensive simulation studies to evaluate their performance under different dose-response scenarios, with and without the existence of the OBD. Based on our results, we recommend the TITE-BOIN12 + TOP combination as the optimal two-stage design for Phase I/II trials.