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Dose Optimization Design for Randomized Phase II Trials With Toxicity and Efficacy Endpoints
Ryuta Tabata1, Tomohiro Ohigashi2, Takashi Sozu2
1Department of Information and Computer Technology, Graduate School of Engineering, Tokyo University of Science, Tokyo, Japan.
Abstract:
Several study designs for identifying optimal biological dose (OBD) have been proposed for phase I, II, and I/II clinical trials, considering toxicity and efficacy of anticancer drugs, especially molecular-targeted therapies and immune checkpoint inhibitors. Among these, the multiple-dose randomized phase II trial (MERIT) design selects OBD candidates using hypothesis testing for toxicity and efficacy outcomes. However, it does not consistently control the type I error rate below the significance level, as the null hypothesis is defined only at specific points in a two-dimensional null space. To address this limitation, we developed a design treating toxicity and efficacy as co-primary endpoints, ensuring strict dose-level type I error control across the entire null space. A Bonferroni correction addressed multiplicity in dose selection, providing overall type I error control. Unlike the existing method, the rejection region is determined analytically rather than by simulation, reducing computational costs. The type I error rate of the existing method exceeds the significance level in regions outside points considered in its sample size calculation. By contrast, the proposed method maintains the type I error rate below the significance level across the null space, though conservatively due to the co-primary endpoint framework and Bonferroni adjustment. Required sample sizes of the proposed method tend to be larger than those of the existing one.
Insights
This study introduces a new clinical trial design for anticancer drugs that strictly controls statistical errors for optimal biological dose (OBD) selection. The novel approach ensures reliable identification of effective and safe drug doses in early-phase trials.
Area of Science:
- Clinical Trials Methodology
- Biostatistics
- Pharmacology
Background:
- Existing optimal biological dose (OBD) identification methods for early-phase anticancer drug trials, such as the multiple-dose randomized phase II trial (MERIT) design, face limitations in controlling statistical error rates.
- These limitations stem from how the null hypothesis is defined, potentially compromising the reliability of OBD selection for molecular-targeted therapies and immune checkpoint inhibitors.
Purpose of the Study:
- To develop and validate a novel clinical trial design that ensures strict type I error control for identifying the optimal biological dose (OBD).
- To address the limitations of existing methods in consistently controlling statistical error rates across the entire null space for toxicity and efficacy.
Main Methods:
- A new design was developed, treating toxicity and efficacy as co-primary endpoints to ensure robust dose-level type I error control.
- Bonferroni correction was applied to manage multiplicity in dose selection, achieving overall type I error control.
- The rejection region is determined analytically, reducing computational burden compared to simulation-based methods.
Main Results:
- The proposed design maintains the type I error rate below the significance level across the entire null space, unlike existing methods.
- The analytical determination of the rejection region offers computational efficiency.
- While conservative due to the co-primary endpoint framework and Bonferroni adjustment, the method ensures statistical rigor.
Conclusions:
- The developed co-primary endpoint design provides a statistically sound framework for OBD selection in phase I/II clinical trials.
- This approach enhances the reliability of identifying optimal biological doses for novel anticancer agents.
- Future research may explore optimizations to balance statistical power and sample size requirements.
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