Optimizing Aggregated N-Of-1 Trial Designs for Predictive Biomarker Validation: Statistical Methods and Theoretical Findings.
Optimizing Aggregated N-Of-1 Trial Designs for Predictive Biomarker Validation: Statistical Methods and Theoretical Findings.
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DOI：
10.3389/fdgth.2020.00013
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发表时间：
2020
影响因子：
--
通讯作者：
Raskind MA
中科院分区：
文献类型：
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作者：
Hendrickson RC;Thomas RG;Schork NJ;Raskind MA
背景和意义：平行组随机对照试验（PG-RCT）是检测不同治疗条件平均改善差异的金标准。然而，PG-RCT 提供的有关个体的信息有限，这使得它们在量化基线测量的生物标志物与治疗反应之间的关系方面表现不佳。在 N-of-1 试验中，个体受试者在治疗条件之间移动，以确定他们对每种治疗的具体反应。聚合 N-of-1 试验分析一组此类参与者，并且可以设计为优化统计功效和临床或后勤限制，例如允许所有参与者从开放标签稳定阶段开始，以方便招募症状更严重的参与者。在这里，我们描述了一组统计模拟研究，比较了四种不同试验设计的功效，以检测基线测量的预测生物标志物与受试者对 PTSD 药物治疗剂哌唑嗪的特定反应之间的关系。方法：从 4 种试验设计中模拟数据：(1) 开放标签； (2)开放标签+盲法停药； (3)传统交叉； (4)开放标签+盲法终止+短暂交叉（N-of-1设计）。设计的长度和评估是匹配的。使用线性混合效应模型分析的主要结果是，生物标志物值与哌唑嗪反应之间是否存在统计学上显着的关联，且 I 型误差为 5%。模拟重复 1,000 次以确定功率和偏差，并使用不同的参数。结果：试验设计 2 和 4 与开放标签设计相比，在受试者数量较少的情况下具有显着更高的功效。试验设计 4 的功率也比试验设计 2 更高。试验设计 4 的功率比传统分频设计略低，但由于引入了结转，功率下降得更快。结论：这些结果表明，在检测预测生物标志物与 PTSD 药物治疗哌唑嗪的临床反应之间的关联方面，从开放标签滴定阶段开始的聚合 N-of-1 试验设计可能比开放标签或开放标签和盲法停药设计提供更好的功效，并且与传统交叉设计相似。这是在允许所有参与者在试验的前 8 周接受开放标签积极治疗的同时实现的。
Background and Significance: Parallel-group randomized controlled trials (PG-RCTs) are the gold standard for detecting differences in mean improvement across treatment conditions. However, PG-RCTs provide limited information about individuals, making them poorly optimized for quantifying the relationship of a biomarker measured at baseline with treatment response. In N-of-1 trials, an individual subject moves between treatment conditions to determine their specific response to each treatment. Aggregated N-of-1 trials analyze a cohort of such participants, and can be designed to optimize both statistical power and clinical or logistical constraints, such as allowing all participants to begin with an open-label stabilization phase to facilitate the enrollment of more acutely symptomatic participants. Here, we describe a set of statistical simulation studies comparing the power of four different trial designs to detect a relationship between a predictive biomarker measured at baseline and subjects' specific response to the PTSD pharmacotherapeutic agent prazosin. Methods: Data was simulated from 4 trial designs: (1) open-label; (2) open-label + blinded discontinuation; (3) traditional crossover; and (4) open label + blinded discontinuation + brief crossover (the N-of-1 design). Designs were matched in length and assessments. The primary outcome, analyzed with a linear mixed effects model, was whether a statistically significant association between biomarker value and response to prazosin was detected with 5% Type I error. Simulations were repeated 1,000 times to determine power and bias, with varied parameters. Results: Trial designs 2 & 4 had substantially higher power with fewer subjects than open label design. Trial design 4 also had higher power than trial design 2. Trial design 4 had slightly lower power than the traditional crossover design, although power declined much more rapidly as carryover was introduced. Conclusions: These results suggest that an aggregated N-of-1 trial design beginning with an open label titration phase may provide superior power over open label or open label and blinded discontinuation designs, and similar power to a traditional crossover design, in detecting an association between a predictive biomarker and the clinical response to the PTSD pharmacotherapeutic prazosin. This is achieved while allowing all participants to spend the first 8 weeks of the trial on open-label active treatment.