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Get Free AccessBasket trial, as an innovative clinical trial design concept, marks the transformation of medical research from the traditional large-scale and single-disease treatment to the precise and individualized treatment. By gradually incorporating the Bayesian method during development, the trial design becomes more scientific and reasonable and increases its efficiency. The fundamental principle of the Bayesian method is the utilization of prior knowledge in conjunction with new observational data to dynamically update the posterior probability. This flexibility enhances the basket trial's capacity to effectively adapt to variations during the research process. Consequently, it enables researchers to dynamically adjust research strategies based on accumulated data and improve the predictive accuracy regarding treatment responses. In addition, the design concept of the basket trial aligns with the traditional Chinese medicine(TCM) principle of "homotherapy for heteropathy". The principle of "homotherapy for heteropathy" emphasizes that under certain conditions, different diseases may have the same treatment. Similarly, basket trials allow using a uniform trial design across multiple diseases, offering enhanced operational and significant practical value in the realm of TCM, particularly within the context of syndrome-based disease research. By introducing basket trials, the design of TCM clinical studies will be more scientific and yield higher-quality evidence. This study systematically categorized various Bayesian methods and models utilized in basket trials, evaluated their strengths and weaknesses, and identified their appropriate application contexts, so as to offer a practical guide for designing basket trials in the realm of TCM.
Shifei Wang, Meijia Li, Haixia Dang, Hao Gu, Jun Liu, Zhong Lin Wang, Yanan Yu (2025). [Exploration of basket trial design with Bayesian method and its application value in traditional Chinese medicine].. , 50(3), DOI: https://doi.org/10.19540/j.cnki.cjcmm.20241029.601.
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Type
Article
Year
2025
Authors
7
Datasets
0
Total Files
0
Language
en
DOI
https://doi.org/10.19540/j.cnki.cjcmm.20241029.601
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