Pulkstenis Erik, Patra Kaushik, Zhang Jianliang
a Department of Biostatistics , MedImmune , Gaithersburg , Maryland , USA.
J Biopharm Stat. 2017;27(3):442-456. doi: 10.1080/10543406.2017.1289947. Epub 2017 Feb 6.
Decision-making is central to every phase of drug development, and especially at the proof of concept stage where risk and evidence must be weighed carefully, often in the presence of significant uncertainty. The decision to proceed or not to large expensive Phase 3 trials has significant implications to both patients and sponsors alike. Recent experience has shown that Phase 3 failure rates remain high. We present a flexible Bayesian quantitative decision-making paradigm that evaluates evidence relative to achieving a multilevel target product profile. A framework for operating characteristics is provided that allows the drug developer to design a proof-of-concept trial in light of its ability to support decision-making rather than merely achieve statistical significance. Operating characteristics are shown to be superior to traditional p-value-based methods. In addition, discussion related to sample size considerations, application to interim futility analysis and incorporation of prior historical information is evaluated.
决策贯穿于药物研发的各个阶段，尤其是在概念验证阶段，在此阶段必须仔细权衡风险和证据，而此时往往存在重大不确定性。决定是否推进大型昂贵的3期试验对患者和申办方都有重大影响。最近的经验表明，3期试验的失败率仍然很高。我们提出了一种灵活的贝叶斯定量决策范式，该范式评估相对于实现多层次目标产品概况的证据。提供了一个操作特征框架，使药物研发者能够根据其支持决策的能力而非仅仅实现统计显著性来设计概念验证试验。结果表明，操作特征优于传统的基于p值的方法。此外，还评估了与样本量考虑、在中期无效性分析中的应用以及纳入既往历史信息相关的讨论。
