Sample size calculations for power analysis are critical for clinical research and trial design, yet their complexity and reliance on statistical expertise create barriers for many researchers. We introduce PowerGPT, an AI-powered system integrating large language models (LLMs) with statistical engines to automate test selection and sample size estimation in trial design. In a randomized trial to evaluate its effectiveness, PowerGPT significantly improved task completion rates (99.3% vs. 88.9% for test selection, 99.3% vs. 77.8% for sample size calculation) and accuracy (94.1% vs. 55.4% in sample size estimation, p < 0.001), while reducing average completion time (4.0 vs. 9.3 minutes, p < 0.001). These gains were consistent across various statistical tests and benefited both statisticians and non-statisticians as well as bridging expertise gaps. Already under deployment across multiple institutions, PowerGPT represents a scalable AI-driven approach that enhances accessibility, efficiency, and accuracy in statistical power analysis for clinical research.
@article{arxiv.2509.12471,
title = {Empowering Clinical Trial Design through AI: A Randomized Evaluation of PowerGPT},
author = {Yiwen Lu and Lu Li and Dazheng Zhang and Xinyao Jian and Tingyin Wang and Siqi Chen and Yuqing Lei and Jiayi Tong and Zhaohan Xi and Haitao Chu and Chongliang Luo and Alexis Ogdie and Brian Athey and Alparslan Turan and Michael Abramoff and Joseph C Cappelleri and Hua Xu and Yun Lu and Jesse Berlin and Daniel I. Sessler and David A. Asch and Xiaoqian Jiang and Yong Chen},
journal= {arXiv preprint arXiv:2509.12471},
year = {2025}
}