Vinnat Valentin, Chiche Jean-Daniel, Demoule Alexandre, Chevret Sylvie
ECSTRRA team, INSERM U1153, Université Paris Cité, Paris, France.
Service de médecine intensive adulte, Centre Hospitalier Universitaire Vaudois, Lausanne, Switzerland.
Contemp Clin Trials Commun. 2023 Apr 14;33:101141. doi: 10.1016/j.conctc.2023.101141. eCollection 2023 Jun.
As we enter the era of precision medicine, the role of adaptive designs, such as response-adaptive randomisation or enrichment designs in drug discovery and development, has become increasingly important to identify the treatment given to a patient based on one or more biomarkers. Tailoring the ventilation supply technique according to the responsiveness of patients to positive end-expiratory pressure is a suitable setting for such a design.
In the setting of marker-strategy design, we propose a Bayesian response-adaptive randomisation with enrichment design based on group sequential analyses. This design combines the elements of enrichment design and response-adaptive randomisation. Concerning the enrichment strategy, Bayesian treatment-by-subset interaction measures were used to adaptively enrich the patients most likely to benefit from an experimental treatment while controlling the false-positive rate.The operating characteristics of the design were assessed by simulation and compared to those of alternate designs.
The results obtained allowed the detection of the superiority of one treatment over another and the presence of a treatment-by-subgroup interaction while keeping the false-positive rate at approximately 5% and reducing the average number of included patients. In addition, simulation studies identified that the number of interim analyses and the burn-in period may have an impact on the performance of the scheme.
The proposed design highlights important objectives of precision medicine, such as determining whether the experimental treatment is superior to another and identifying wheter such an efficacy could depend on patient profile.
随着我们进入精准医学时代，适应性设计（如药物研发中的反应适应性随机化或富集设计）在根据一种或多种生物标志物确定给予患者的治疗方案方面的作用变得越来越重要。根据患者对呼气末正压的反应性来调整通气供应技术是这种设计的一个合适场景。
在标志物策略设计的背景下，我们提出一种基于成组序贯分析的带有富集设计的贝叶斯反应适应性随机化方法。该设计结合了富集设计和反应适应性随机化的要素。关于富集策略，采用贝叶斯亚组治疗交互作用测量方法，在控制假阳性率的同时，自适应地富集最有可能从实验性治疗中获益的患者。通过模拟评估该设计的操作特性，并与其他设计进行比较。
所获得的结果能够检测出一种治疗相对于另一种治疗的优越性以及治疗亚组交互作用的存在，同时将假阳性率保持在约5%，并减少纳入患者的平均数量。此外，模拟研究表明，中期分析的次数和预烧期可能会对该方案的性能产生影响。
所提出的设计突出了精准医学的重要目标，如确定实验性治疗是否优于另一种治疗，以及确定这种疗效是否可能取决于患者特征。
