A flexible multi-metric Bayesian framework for decision-making in Phase II multi-arm multi-stage studies.
A flexible multi-metric Bayesian framework for decision-making in Phase II multi-arm multi-stage studies.
复制标题
用于第二阶段多臂多阶段研究决策的灵活多度量贝叶斯框架。
DOI：
10.1002/sim.9961
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发表时间：
2024
影响因子：
2
通讯作者：
Phillips,PatrickPJ
中科院分区：
文献类型：
--
作者：
Dufault,SuzanneM;Crook,AngelaM;Rolfe,Katie;Phillips,PatrickPJ
我们提出了一个多指标的灵活贝叶斯框架，以支持在多臂多阶段II期临床试验中有效的中期决策。多组多期II期研究提高了药物开发的效率，但由于样本量通常较低，随访时间可能较短，因此关于某组无效或可取性的早期决定存在相当大的风险。此外，由于基于治疗反应的生物标志物的中间结果很少能完美地替代主要结果，而且不同的试验利益相关者可能具有不同的风险承受水平，因此单一的假设检验不足以全面总结所收集证据的状态。我们提出了一个贝叶斯框架，该框架由基于点估计、不确定性和对期望阈值（目标产品概况）的证据的多个指标组成，用于(1)武器的排名和(2)每个武器与内部控制的比较。我们以针对新型结核病分支的大型公私合作伙伴关系为例，通过模拟研究发现，我们的多指标框架为样本量低至每分支30例患者的决策提供了足够的信心，即使中间结果与主要结果只有适度的相关性。我们对试验设计和决策程序的重新构建得到了研究伙伴的好评，是一种更有效评估新疗法的实用方法。
We propose a multi‐metric flexible Bayesian framework to support efficient interim decision‐making in multi‐arm multi‐stage phase II clinical trials. Multi‐arm multi‐stage phase II studies increase the efficiency of drug development, but early decisions regarding the futility or desirability of a given arm carry considerable risk since sample sizes are often low and follow‐up periods may be short. Further, since intermediate outcomes based on biomarkers of treatment response are rarely perfect surrogates for the primary outcome and different trial stakeholders may have different levels of risk tolerance, a single hypothesis test is insufficient for comprehensively summarizing the state of the collected evidence. We present a Bayesian framework comprised of multiple metrics based on point estimates, uncertainty, and evidence towards desired thresholds (a Target Product Profile) for (1) ranking of arms and (2) comparison of each arm against an internal control. Using a large public‐private partnership targeting novel TB arms as a motivating example, we find via simulation study that our multi‐metric framework provides sufficient confidence for decision‐making with sample sizes as low as 30 patients per arm, even when intermediate outcomes have only moderate correlation with the primary outcome. Our reframing of trial design and the decision‐making procedure has been well‐received by research partners and is a practical approach to more efficient assessment of novel therapeutics.