Marchenko Olga, Miller Joel, Parke Tom, Perevozskaya Inna, Qian Jiang, Wang Yanping
1 Innovation, Quintiles, Durham, NC, USA.
2 Biometrics, Lilly Oncology, Bridgewater, NJ, USA.
Ther Innov Regul Sci. 2013 Sep;47(5):602-612. doi: 10.1177/2168479013495685.
The design of an oncology clinical program is much more challenging than the design of a single study. The standard approach has been proven to be not very successful during the past decade; the failure rate of phase 3 studies in oncology is about 66%. Improving the development strategy by applying innovative statistical methods is one of the major objectives for study teams designing and supporting oncology clinical programs. However, evaluating trial design alternatives is difficult; the designs may have different advantages-better power, better type I error control, shorter duration, or more accuracy-and their relative performance may depend on assumptions about the drugs' performance. Evaluating different early phase designs in particular suffers from both these problems. This paper is built on the work of the DIA's Adaptive Design Scientific Working Group oncology subteam on an Adaptive Program. With representatives from a number of institutions, this group compared 4 hypothetical oncology development programs that each was to select between 2 treatments and decide whether to proceed to phase 3, using probability of the clinical program's success and expected net present value (eNPV). Simulated scenarios were used to motivate and illustrate the key ideas. For the development strategies, we believed that the eNPV showed a distinct and robust improvement for each successive strategy.
肿瘤学临床项目的设计比单个研究的设计更具挑战性。在过去十年中，标准方法已被证明不太成功；肿瘤学3期研究的失败率约为66%。应用创新统计方法改进开发策略是设计和支持肿瘤学临床项目的研究团队的主要目标之一。然而，评估试验设计的替代方案很困难；这些设计可能有不同的优势——更高的检验效能、更好的I型错误控制、更短的持续时间或更高的准确性——而且它们的相对性能可能取决于对药物性能的假设。评估不同的早期阶段设计尤其会遇到这两个问题。本文基于DIA适应性设计科学工作组肿瘤学分组关于适应性项目的工作。该小组有来自多个机构的代表，他们比较了4个假设的肿瘤学开发项目，每个项目都要在2种治疗方法之间进行选择，并决定是否进入3期，使用临床项目成功的概率和预期净现值（eNPV）。模拟场景被用来激发和说明关键思想。对于开发策略，我们认为每个后续策略的eNPV都有明显且稳健的提升。
