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Translational Medicine / 转化医学early phase trials

Ying Yuan

袁颖

PhD

🏢The University of Texas MD Anderson Cancer Center(德克萨斯大学MD安德森癌症中心)🌐USA

Professor, Department of Biostatistics生物统计学系教授

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h-index
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Key Papers
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Awards
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Key Contributions

👥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.

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🧪Research Fields 研究领域

Bayesian Adaptive Designs贝叶斯自适应设计
BOIN DesignBOIN设计
Dose Escalation Methods剂量递增方法
Phase I Trial StatisticsI期临床试验统计
mTPI DesignmTPI设计
Optimal Interval Designs最优区间设计

🎓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

[1]

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.

[2]

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.

[3]

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

🏆ASA Mortimer Spiegelman Award
🏆ISCB Distinguished Researcher Award in Biometrics
🏆MD Anderson Faculty Achievement Award in Research
🏆Fellow, American Statistical Association

📄Data Sources 数据来源

Last updated: 2026-01-15 | All information from publicly available academic sources

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