论文标题
贝叶斯反应自适应剂量发现和比较有效性试验
A Bayesian response-adaptive dose finding and comparative effectiveness trial
论文作者
论文摘要
目的：治疗的组合可以单独提供额外的好处。但是，这些组合的试验比新疗法的开发较低，这可以限制资金，时间表和患者的可用性。本文开发了一种新颖的试验设计，以促进结合II期和III期试验元素的新型组合疗法的评估。方法：该试验使用响应自适应随机化来增加有关成功的新型药物组合和贝叶斯剂量反应建模的信息，以对相关比较器进行最成功的剂量组合进行比较效应分析。我们使用仿真方法来评估选择正确的最佳剂量组合，该设计的操作特征和预测能力的可能性，用于小儿急诊室的疼痛管理和镇静试验。结果：有410名参与者，随机化比率的5个临时更新，三种剂量组合的有效性为0.93、0.88和0.83，我们有83％的机会将最大数量的患者与有效性最高的药物随机。基于这种适应性随机化程序，当最佳组合治疗的有效性的可能性为0.9时，比较有效性分析的I型误差小于5％，有93％的机会正确地得出了不劣质性。在这种情况下，该试验有77％的机会实现其双重剂量发现和比较有效性的目标。最后，该试验的贝叶斯预测能力超过90％。结论：拟议的试验具有很高的潜力，可以在可行的招聘水平范围内实现双重研究目标，从而最大程度地减少行政负担和招聘时间进行试验。
Aims: Combinations of treatments can offer additional benefit over the treatments individually. However, trials of these combinations are lower priority than the development of novel therapies, which can restrict funding, timelines and patient availability. This paper develops a novel trial design to facilitate the evaluation of novel combination therapies that combines elements of phase II and phase III trials. Methods: This trial uses response adaptive randomisation to increase the information collected about successful novel drug combinations and Bayesian dose-response modelling to undertake a comparative-effectiveness analysis for the most successful dose combination against a relevant comparator. We used simulation methods to evaluate the probability of selecting the correct optimal dose combination, the operating characteristics and predictive power of this design for a trial in pain management and sedation in paediatric emergency departments. Results: With 410 participants, 5 interim updates of the randomisation ratio and a probability of effectiveness of 0.93, 0.88 and 0.83 for the three dose combinations, we have an 83% chance of randomising the largest number of patients to the drug with the highest probability of effectiveness. Based on this adaptive randomisation procedure, the comparative effectiveness analysis has a type I error of less than 5% and a 93% chance of correctly concluding non-inferiority when the probability of effectiveness for the optimal combination therapy is 0.9. In this case, the trial has a 77% chance of meeting its dual aims of dose finding and comparative effectiveness. Finally, the Bayesian predictive power of the trial is over 90%. Conclusion: The proposed trial has high potential to meet the dual study objectives within a feasible level of recruitment, minimising the administrative burden and recruitment time for a trial.
