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Frequentist Identification of Effective Baskets via the Generalized Information Criteria in Oncology Phase 2 Trials

Shunya Tanaka1,2, Ryota Ohara1, Satoshi Hattori1,3

  • 1Department of Biomedical Statistics, Graduate School of Medicine, The University of Osaka, Osaka, Japan.

Statistics in Medicine
|July 14, 2026
PubMed

Insights

This study introduces a novel statistical method for oncology basket trials, improving how treatment effectiveness is evaluated across multiple tumor subtypes. The new approach enhances information sharing between baskets, leading to more efficient and reliable drug development.

Area of Science:

  • Oncology
  • Biostatistics
  • Clinical Trial Design

Background:

  • Molecular-targeted therapies offer potential for broad efficacy across tumor types.
  • Basket trials enable simultaneous evaluation of drugs across multiple subtypes (baskets).
  • Existing statistical methods for basket trials are often complex Bayesian approaches.

Purpose of the Study:

  • To address limitations in existing statistical methods for oncology basket trials.
  • To develop a more efficient and robust method for identifying effective treatments across tumor subtypes.
  • To improve the borrowing of information across different tumor baskets in clinical trials.

Main Methods:

  • Development of an alternative Generalized Information Criterion (GIC) for basket trials.
  • Utilizing a one-sample Mantel-Haenszel procedure for hypothesis testing and effect estimation.
  • Comparison of the new GIC with the original GIC through numerical simulations.

Main Results:

  • The proposed new GIC overcomes an undesirable feature of the previous GIC.
  • The new GIC demonstrates more efficient information borrowing across baskets.
  • Numerical studies confirm the superior performance of the new GIC compared to the original.

Conclusions:

  • The novel GIC provides a more effective statistical tool for oncology basket trials.
  • This advancement facilitates more efficient drug development by better leveraging data across tumor subtypes.
  • The improved method supports more accurate identification of molecular-targeted therapies with broad applicability.
Keywords:
dual consistencygeneralized information criterionone‐sample Mantel‐Haenszel estimator

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