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, TPP) 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.

翻译：我们提出一种多指标灵活贝叶斯框架，用于支持多臂多阶段II期临床试验中的高效中期决策。多臂多阶段II期研究提高了药物研发的效率，但由于样本量通常较少且随访期可能较短，针对特定试验组的无效性或可行性进行的早期决策存在较大风险。此外，由于基于治疗反应生物标志物的中期结局很少能完美替代主要终点指标，且不同试验利益相关方具有不同风险容忍度，单一假设检验不足以全面汇总已有证据。我们提出一个由基于点估计、不确定性及目标阈值（目标产品特征，TPP）的多个指标构成的贝叶斯框架，用于：1)对各试验组排序；2)将每组与内部对照进行比较。以针对新型结核病药物研发的大型公私合作项目作为驱动案例，通过模拟研究发现：即使在中期结局与主要结局仅呈中度相关的情况下，我们的多指标框架仍能在每组样本量低至30例时提供充分的决策可信度。我们对试验设计及决策流程的重新构建已获得研究合作伙伴的广泛认可，这是实现新型疗法更高效评估的实用方法。