Bayesian statistics is an approach for learning from evidences as it accumulates, combining prior distribution with current information on a quantity of interest, in which posterior distribution and inferences are being updated each time new data become available using Bayes' Theorem. Though frequentist approach has dominated medical studies, Bayesian approach has been more and more widely recognized by its flexibility and efficiency. Research and development (R&D) on anti-cancer new drugs have been so hot globally in recent years in spite of relatively high failure rate. It is the common demand of pharmaceutical enterprises and researchers to identify the optimal dose, regime and right population in the early-phase R&D stage more accurately and efficiently, especially when the following three major changes have been observed. The R&D on anticancer drugs have transformed from chemical drugs to biological products, from monotherapy to combination therapy, and the study design has also gradually changed from traditional way to innovative and adaptive mode. This also raises a number of subsequent challenges on decision-making of early R&D, such as inability to determine MTD, flexibility to deal with delayed toxicity, delayed response and dose-response changing relationships. It is because of the above emerging changes and challenges that the Bayesian approach is getting more and more attention from the industry. At least, Bayesian approach has more information for decision-making, which could potentially help enterprises achieve higher efficiency, shorter period and lower investment. This study also expounds the application of Bayesian statistics in the early R&D on anticancer new drugs, and compares and analyzes its idea and application scenarios with frequentist statistics, aiming to provide macroscopic and systematic reference for all related stakeholders. .
【中文题目：贝叶斯方法在肿瘤新药早期临床研发中 的发展与应用】 【中文摘要：贝叶斯学派是通过综合未知参数的先验信息与样本信息，依据贝叶斯定理，求出后验分布，根据后验分布推断未知参数的统计方法。相比频率派，贝叶斯学派更加灵活、高效。肿瘤新药是全球研发的热点，但同时也存在高失败率的风险。在肿瘤新药早期研发中，高效寻找最佳剂量、优势人群、估计疗效和成功率是医药企业和研究者的共同需求。近年来，肿瘤新药研发呈现化学药物生物制品转变、单药治疗向联合治疗转变、传统设计向创新设计转变等新趋势；伴随出现的各种挑战，包括无法找到最高耐受剂量、延迟毒性、延迟反应、剂量疗效关系变化、剂量组合众多等。基于贝叶斯方法，恰当借用先验信息，能有效帮助企业在肿瘤早期研发中，实现从传统研发模式（高投入、长周期、低效率）向现代研发模式（低投入、短周期、高效率）的转变。研究还进行了贝叶斯方法在肿瘤新药早期研发的进展阐述，与频率派的理念、应用场景的比较分析，可为医药研发的所有从业人员提供宏观、系统的参考。 】 【中文关键词：早期试验；贝叶斯； 统计设计；肿瘤】.
Keywords: Bayesian; Exploratory trial; Neoplasm; Statistical design.