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

Hanan Hammouri (), Muna Salman, Mohammed Ali and Ruwa Abdel Muhsen
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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

Mathematics, 2025, vol. 13, issue 8, 1-23

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.

Keywords: statistical methods; sequential group test; O’Brien and Fleming; type I error and power; simulations; SAS software; hypotheses testing; biostatistics; categorical data analysis; Neyman allocation (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2025
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