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Advances in Clinical Trial Design: Employing Adaptive Multiple Testing and Neyman Allocation for Unequal Samples

Author

Listed:
  • Hanan Hammouri

    (Department of Mathematics and Statistics, Jordan University of Science and Technology, Irbid 22110, Jordan)

  • Muna Salman

    (Department of Mathematics and Statistics, Jordan University of Science and Technology, Irbid 22110, Jordan)

  • Mohammed Ali

    (Department of Mathematics and Statistics, Jordan University of Science and Technology, Irbid 22110, Jordan)

  • Ruwa Abdel Muhsen

    (Department of Mathematical Sciences, New Mexico State University, Las Cruces, NM 88001, USA)

Abstract

This study introduces a new method that combines three distinct approaches for comparing two treatments: Neyman allocation, the O’Brien and Fleming multiple testing procedure, and a system of different sample weights at different stages. This new approach is called the Neyman Weighted Multiple Testing Procedure (NWMP). Each of these adaptive designs “individually” has proven beneficial for clinical research by removing constraints that can limit clinical trials. The advantages of these three methods are merged into a single, innovative approach that demonstrates increased efficiency in this work. The multiple testing procedure allows for trials to be stopped before their chosen time frame if one treatment is more effective. Neyman allocation is a statistically sound method designed to enhance the efficiency and precision of estimates. It strategically allocates resources or sample sizes to maximize the quality of statistical inference, considering practical constraints. Additionally, using different weights in this method provides greater flexibility, allowing for the effective distribution of sample sizes across various stages of the research. This study demonstrates that the new method maintains similar efficiency in terms of the Type I error rate and statistical power compared to the O’Brien and Fleming test while offering additional flexibility. Furthermore, the research includes examples of both real and hypothetical cases to illustrate the developed procedure.

Suggested Citation

  • Hanan Hammouri & Muna Salman & Mohammed Ali & Ruwa Abdel Muhsen, 2025. "Advances in Clinical Trial Design: Employing Adaptive Multiple Testing and Neyman Allocation for Unequal Samples," Mathematics, MDPI, vol. 13(8), pages 1-23, April.
    • Handle: RePEc:gam:jmathe:v:13:y:2025:i:8:p:1273-:d:1633408
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    References listed on IDEAS

    as
    1. Hanan Hammouri & Mohammed Ali & Marwan Alquran & Areen Alquran & Ruwa Abdel Muhsen & Belal Alomari, 2023. "Adaptive Multiple Testing Procedure for Clinical Trials with Urn Allocation," Mathematics, MDPI, vol. 11(18), pages 1-20, September.
    2. Belmiro P. M. Duarte & Anthony C. Atkinson & David Pedrosa & Marlena van Munster, 2024. "Compound Optimum Designs for Clinical Trials in Personalized Medicine," Mathematics, MDPI, vol. 12(19), pages 1-20, September.
    3. Hanan Hammouri & Marwan Alquran & Ruwa Abdel Muhsen & Jaser Altahat, 2022. "Optimal Weighted Multiple-Testing Procedure for Clinical Trials," Mathematics, MDPI, vol. 10(12), pages 1-19, June.
    4. William F. Rosenberger & Nigel Stallard & Anastasia Ivanova & Cherice N. Harper & Michelle L. Ricks, 2001. "Optimal Adaptive Designs for Binary Response Trials," Biometrics, The International Biometric Society, vol. 57(3), pages 909-913, September.
    5. Alessandro Baldi Antognini & Alessandra Giovagnoli, 2010. "Compound optimal allocation for individual and collective ethics in binary clinical trials," Biometrika, Biometrika Trust, vol. 97(4), pages 935-946.
    6. A. C. Atkinson, 2015. "Optimum designs for two treatments with unequal variances in the presence of covariates," Biometrika, Biometrika Trust, vol. 102(2), pages 494-499.
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