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| Auteurs principaux: | , , , , , , , , , , , , , , , , , , , , , , |
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| Format: | Preprint |
| Publié: |
2025
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| Sujets: | |
| Accès en ligne: | https://arxiv.org/abs/2509.12471 |
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| _version_ | 1866916950939009024 |
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| author | Lu, Yiwen Li, Lu Zhang, Dazheng Jian, Xinyao Wang, Tingyin Chen, Siqi Lei, Yuqing Tong, Jiayi Xi, Zhaohan Chu, Haitao Luo, Chongliang Ogdie, Alexis Athey, Brian Turan, Alparslan Abramoff, Michael Cappelleri, Joseph C Xu, Hua Lu, Yun Berlin, Jesse Sessler, Daniel I. Asch, David A. Jiang, Xiaoqian Chen, Yong |
| author_facet | Lu, Yiwen Li, Lu Zhang, Dazheng Jian, Xinyao Wang, Tingyin Chen, Siqi Lei, Yuqing Tong, Jiayi Xi, Zhaohan Chu, Haitao Luo, Chongliang Ogdie, Alexis Athey, Brian Turan, Alparslan Abramoff, Michael Cappelleri, Joseph C Xu, Hua Lu, Yun Berlin, Jesse Sessler, Daniel I. Asch, David A. Jiang, Xiaoqian Chen, Yong |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_12471 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Empowering Clinical Trial Design through AI: A Randomized Evaluation of PowerGPT Lu, Yiwen Li, Lu Zhang, Dazheng Jian, Xinyao Wang, Tingyin Chen, Siqi Lei, Yuqing Tong, Jiayi Xi, Zhaohan Chu, Haitao Luo, Chongliang Ogdie, Alexis Athey, Brian Turan, Alparslan Abramoff, Michael Cappelleri, Joseph C Xu, Hua Lu, Yun Berlin, Jesse Sessler, Daniel I. Asch, David A. Jiang, Xiaoqian Chen, Yong Artificial Intelligence 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. |
| title | Empowering Clinical Trial Design through AI: A Randomized Evaluation of PowerGPT |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2509.12471 |