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Curtailed procedures for binomial random-sized subset selection.

Yifang Zhang1, Pinyuen Chen1

  • 1Department of Mathematics, Syracuse University, Syracuse, New York, USA.

Journal of Biopharmaceutical Statistics
|November 13, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces a new curtailed subset selection procedure for clinical trials. It significantly reduces sample sizes while maintaining accuracy, improving efficiency in biopharmaceutical research.

Keywords:
Subset selectionbinomial distributionsequential procedures

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Area of Science:

  • Biostatistics
  • Clinical Trial Design
  • Pharmaceutical Research

Background:

  • Traditional subset selection methods in clinical trials lack early stopping rules.
  • This can lead to inefficiencies and prolonged patient exposure to non-beneficial treatments.

Purpose of the Study:

  • To introduce a curtailed subset selection procedure for binomial populations within a frequentist framework.
  • To develop a statistically driven stopping rule for efficient treatment selection.
  • To provide a tool for optimizing subset selection in biopharmaceutical research.

Main Methods:

  • Developed a curtailed subset selection procedure inspired by Gupta and Sobel (1960) and Bechhofer and Kulkarni (1982).
  • Incorporated a mathematically driven stopping rule to terminate sampling when non-leading treatments cannot surpass the leader.
  • Derived formulas for probability of correct selection and expected sample size, with an optional randomization extension.

Main Results:

  • The proposed curtailed procedure maintains comparable accuracy to existing methods.
  • Substantially reduces expected sample sizes compared to traditional procedures.
  • Simulation studies and clinical trial examples demonstrate practical benefits and ease of implementation.

Conclusions:

  • The new curtailed subset selection procedure offers a statistically rigorous and efficient tool for clinical trials.
  • It optimizes treatment selection by minimizing unnecessary sampling and patient exposure.
  • This method enhances decision-making in biopharmaceutical research and development.