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.

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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