Huster W J, Enas G G
Eli Lilly and Company, Lilly Corporate Center, Indianapolis, Indiana 46285, USA.
Stat Med. 1998;17(15-16):1829-38. doi: 10.1002/(sici)1097-0258(19980815/30)17:15/16<1829::aid-sim985>3.0.co;2-k.
Much has been published on various aspects of data analysis and reporting from clinical trials within the biopharmaceutical environment. This ranges from regulatory guidelines on the format and content of registration dossiers to recommendations on data presentation and the statistical methodologies that are appropriate for the diverse types of data one observes in clinical trials. Little has been written about designing a clinical trial analysis and reporting package that focuses on the decisions that must be made throughout the drug development process. Pharmaceutical companies today are under enormous pressure to develop drugs quickly and (cost-) efficiently. Because of this, drugs often move into the later phases of drug development before evidence from prior phases is completely understood. This provides a challenge to clinical trialists to design and execute a clinical trial programme which can expedite drug development. The statistician, as a clinical trialist, must strive to determine the optimum analytical methodology that facilitates decision making for this clinical trial programme. This paper proposes a new framework for the assessment of efficacy in drug development called the 'one programme, one p-value' framework. This framework will accelerate drug development by providing clear criteria for the decisions which must be made along the way. The 'one programme, one p-value' framework is based on the notion that the clinical trial programme comprises exploratory and confirmatory phases. The use of the likelihood function in the exploratory phase facilitates the decision whether (or when) to move into the confirmatory phase. The confirmatory phase consists of one confirmatory trial with a single hypothesis test of the drug's efficacy; hence 'one p-value'. Sponsor interaction with regulatory agencies is necessary at each decision point. Finally, the paper considers how analysis and reporting of efficacy data can be accomplished from a clinical trial programme as described.
关于生物制药环境中临床试验数据分析和报告的各个方面，已经发表了大量内容。这涵盖了从注册文件格式和内容的监管指南到数据呈现建议以及适用于临床试验中观察到的各种数据类型的统计方法。关于设计一个专注于药物开发全过程中必须做出的决策的临床试验分析和报告包的内容则鲜有著述。如今，制药公司面临着迅速且（成本）高效地研发药物的巨大压力。因此，药物常常在前期阶段的证据尚未完全理解之前就进入了后期开发阶段。这给临床试验人员带来了挑战，要求他们设计并执行一个能够加速药物开发的临床试验方案。作为临床试验人员的统计学家必须努力确定有助于为该临床试验方案做出决策的最佳分析方法。本文提出了一种用于药物开发疗效评估的新框架，即“一个方案，一个p值”框架。该框架将通过为沿途必须做出的决策提供明确标准来加速药物开发。“一个方案，一个p值”框架基于这样一种理念，即临床试验方案包括探索性和确证性阶段。在探索性阶段使用似然函数有助于决定是否（或何时）进入确证性阶段。确证性阶段由一项对药物疗效进行单一假设检验的确证性试验组成；因此是“一个p值”。在每个决策点，申办方与监管机构的互动都是必要的。最后，本文探讨了如何根据所描述的临床试验方案来完成疗效数据的分析和报告。
