AUTHOR=Zhu Yuxi , Du Shi , Xia Peihe 

TITLE=GSD-SSR: an integrated framework for power analysis in IRB proposals using group sequential design and sample size re-estimation

JOURNAL=Frontiers in Applied Mathematics and Statistics

VOLUME=Volume 11 - 2025

YEAR=2025

URL=https://www.frontiersin.org/journals/applied-mathematics-and-statistics/articles/10.3389/fams.2025.1611205

DOI=10.3389/fams.2025.1611205

ISSN=2297-4687

ABSTRACT=Accurate sample size estimation is a cornerstone of successful Institutional Review Board (IRB) proposals, as it establishes the feasibility of clinical studies and ensures they are sufficiently powered to detect meaningful effects. Underestimating sample size poses the risk of insufficient statistical power, compromising the ability to identify significant outcomes. Conversely, overestimating sample size can lead to prolonged data collection, wasting valuable time and resources. One of the primary challenges in sample size estimation lies in the uncertainty surrounding variance and effect size before the study begins. Group Sequential Design with Sample Size Re-estimation (GSD-SSR) effectively addresses this issue by utilizing interim data at predefined stages to refine these estimates. GSD-SSR enables dynamic adjustments to sample size during the study, optimizing resource allocation and improving overall efficiency. We offer a comprehensive introduction to the theoretical background of GSD-SSR and provide step-by-step guidance for its practical application in clinical research. To further facilitate adoption, we have developed a user-friendly online platform that streamlines the GSD-SSR process and integrates it seamlessly into IRB proposals. By incorporating GSD-SSR into the power analysis of IRB proposals, researchers can significantly increase the likelihood of successful clinical studies while enhancing budget efficiency and optimizing timelines.