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

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.

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Publication Details

Journal
Frontiers in Applied Mathematics and Statistics
Published
2025-07-14
DOI
https://doi.org/10.3389/fams.2025.1611205
Primary Topic
Statistical Methods in Clinical Trials
Type
article
Field-Weighted Citation Impact
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article

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

Shi Du, Pengcheng Xia, Yuxi Zhu
Frontiers in Applied Mathematics and Statistics
Statistical Methods in Clinical Trials
article

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

Shi Du, Pengcheng Xia, Yuxi Zhu
article en

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.

Frontiers in Applied Mathematics and StatisticsVol. 11
Soochow University (CN), Rainbow Babies & Children's Hospital (US), The Ohio State University (US)
Openalex Percentile: Top 83%
Statistical Methods in Clinical Trials
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GSD-SSR: an integrated framework for power analysis in IRB proposals using group sequential design and sample size re-estimation — Shi Du, Pengcheng Xia, et al. · Frontiers in Applied Mathematics and Statistics (2025) | TGRS Research Map | TGRS