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PMED: Optimal Bayesian Platform Trial Design with Multiple Endpoints
Tian He1, Rachael Liu2, Meizi Liu2
1Department of Biostatistics and Health Data Science, Indiana University, Indianapolis, Indiana, USA.
Abstract:
In oncology drug development, indication selection and optimal dose identification are the primary objectives for the early phase of clinical trials and could significantly impact the probability of success. Master protocols, e.g., basket trial, umbrella trial, and platform trial, have become popular in practice considering the connection of trial designs with multiple indications and treatment candidates. They also enable the optimization of operational resources and maximize the capability of data-driven decision-making. However, most of the available designs are developed with the efficacy endpoint only for treatment effect estimation and testing, without consideration of the safety end point. Thus, it often lacks a comprehensive quantitative framework to allow optimal treatment selection, which could put future development at risk. We propose an optimal Bayesian platform trial design with multiple end points (PMED) to characterize the overall benefit-risk profile. The design is further extended to allow treatment and indication selection within and across arms, with continuous monitoring on multiple interim analyses for futility. In addition, we propose dynamic borrowing across arms to increase the efficiency and accuracy of estimation given the level of similarity across arms. A hierarchical hypothesis structure is utilized to achieve optimal indication and treatment combination selection by controlling family-wise error. Through simulation studies, we show that PMED is a robust design under the studied scenarios with superb power and controlled family-wise error rate.
Insights
This study introduces an optimal Bayesian platform trial design (PMED) for oncology, integrating multiple endpoints to assess benefit-risk profiles. It enhances treatment and indication selection while managing risks and improving decision-making in early-phase drug development.
Area of Science:
- Clinical Trials
- Biostatistics
- Oncology Drug Development
Background:
- Early-phase oncology trials focus on indication and dose selection, crucial for success.
- Master protocols (basket, umbrella, platform trials) are popular but often lack safety endpoint integration.
- Existing designs may not offer a comprehensive framework for optimal treatment selection, risking future development.
Purpose of the Study:
- To propose an optimal Bayesian platform trial design with multiple endpoints (PMED) for characterizing the overall benefit-risk profile in oncology.
- To extend the design for treatment and indication selection within and across arms, including futility monitoring.
- To introduce dynamic borrowing across arms for enhanced estimation efficiency and accuracy.
Main Methods:
- Developed an optimal Bayesian platform trial design (PMED) incorporating multiple endpoints.
- Integrated treatment and indication selection with continuous interim analyses for futility.
- Employed dynamic borrowing across arms and a hierarchical hypothesis structure.
- Utilized simulation studies to evaluate design performance.
Main Results:
- The proposed PMED design effectively characterizes the overall benefit-risk profile.
- The design allows for optimal treatment and indication selection within and across arms.
- Dynamic borrowing enhances estimation efficiency and accuracy.
- Simulation studies demonstrated the robustness, superb power, and controlled family-wise error rate of PMED.
Conclusions:
- PMED provides a comprehensive quantitative framework for optimal treatment selection in oncology drug development.
- The design addresses the limitations of existing protocols by integrating efficacy and safety endpoints.
- PMED improves decision-making, increases trial efficiency, and reduces the risk of future development failures.
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