Ying Yuan
袁颖
PhD
Professor, Department of Biostatistics生物统计学系教授
👥Biography 个人简介
Ying Yuan, PhD is a Professor of Biostatistics at MD Anderson Cancer Center and one of the world's leading experts in statistical design of early-phase oncology clinical trials. He is the inventor of the Bayesian Optimal Interval (BOIN) design, which has become one of the most widely adopted dose escalation frameworks globally, implemented in hundreds of phase I trials across industry and academia. The BOIN design offers the practical simplicity of rule-based designs (like 3+3) while achieving near-optimal operating characteristics approaching the continual reassessment method (CRM). Dr. Yuan also developed the modified Toxicity Probability Interval (mTPI) design and its successor mTPI-2, as well as the BOIN12 extension for efficacy-toxicity tradeoff optimization. His methodological contributions span accelerated titration designs, keyboard designs, and drug combination dose-finding. He has published landmark papers in statistics, biostatistics, and clinical oncology journals, and his open-source software (BOIN suite on CRAN and MD Anderson Phase I Design website) has enabled global adoption of his methods. Dr. Yuan has collaborated on over 100 phase I trials and consults with the FDA on adaptive dose-finding design standards.
🧪Research Fields 研究领域
🎓Key Contributions 主要贡献
BOIN (Bayesian Optimal Interval) Design
Invented the BOIN dose escalation design, which achieves near-optimal dose finding with simple decision tables, making it easy to implement while outperforming the 3+3 design in accuracy of MTD identification. BOIN has been adopted in hundreds of industry and academic trials globally and recommended by FDA.
mTPI and mTPI-2 Designs
Developed the modified Toxicity Probability Interval (mTPI) design and its improved version mTPI-2, providing coherent Bayesian interval-based dose escalation rules that overcome irregularities of the original TPI and improve on 3+3.
BOIN12: Efficacy-Toxicity Dose Optimization
Extended BOIN to the BOIN12 design for dose optimization considering both toxicity and efficacy outcomes, enabling optimal biological dose selection beyond MTD-focused designs in the era of targeted and immunotherapy agents.
Drug Combination Dose Finding
Developed Bayesian adaptive designs for drug combination dose finding, including the BOIN combination design (BOIN-comb), addressing one of the most complex challenges in early oncology drug development.
Representative Works 代表性著作
Bayesian optimal interval design: a simple and well-performing design for Phase I oncology trials
Clinical Cancer Research (2016)
Landmark paper introducing the BOIN design, demonstrating its near-optimal operating characteristics and practical simplicity compared to 3+3 and CRM in extensive simulations.
mTPI-2: an improved toxicity probability interval design for phase I drug/combination trials
Statistics in Medicine (2018)
Introduced mTPI-2 addressing coherence issues in the original mTPI design while maintaining Bayesian interval-based decision framework.
BOIN12: Bayesian optimal interval phase I/II trial design for utility-based dose finding in immunotherapy and targeted therapies
JCO Precision Oncology (2022)
Extension of BOIN framework to simultaneous toxicity-efficacy optimization, enabling optimal dose selection for agents without a clear MTD endpoint.
🏆Awards & Recognition 奖项与荣誉
📄Data Sources 数据来源
Last updated: 2026-01-15 | All information from publicly available academic sources
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