Sequential monitoring of response-adaptive randomized clinical trials
响应自适应随机临床试验的序贯监测AI 解读 ↓
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响应自适应随机临床试验的序贯监测
- 研究问题
- 如何在响应自适应随机分配下开展序贯检验，同时兼顾第一类错误控制、检验功效以及向较优治疗分配更多患者？
- 方法与思路
- 作者结合响应自适应随机化与序贯监测，研究检验统计量的布朗运动极限及渐近联合分布，并以模拟和临床试验重新设计作说明。
- 材料陈述的贡献
- 摘要称统计量渐近满足标准序贯联合分布，可通过适当边界实现错误控制；模拟显示该程序兼顾功效、样本量和治疗失败人数。
- 阅读关注点
- 应核对随机分配规则、统计量构造、过程收敛条件与监测边界，并检查模拟中的错误率、功效及失败人数如何比较。
- 阅读边界
- 仅据稿件版本未注明的索引摘要；具体正则条件、边界及模拟数值未提供，渐近控制不能直接保证有限样本表现。
原文摘要
Clinical trials are complex and usually involve multiple objectives such as controlling type I error rate, increasing power to detect treatment difference, assigning more patients to better treatment, and more. In literature, both response-adaptive randomization (RAR) procedures (by changing randomization procedure sequentially) and sequential monitoring (by changing analysis procedure sequentially) have been proposed to achieve these objectives to some degree. In this paper, we propose to sequentially monitor response-adaptive randomized clinical trial and study it’s properties. We prove that the sequential test statistics of the new procedure converge to a Brownian motion in distribution. Further, we show that the sequential test statistics asymptotically satisfy the canonical joint distribution defined in Jennison and Turnbull (2000). Therefore, type I error and other objectives can be achieved theoretically by selecting appropriate boundaries. These results open a door to sequentially monitor response-adaptive randomized clinical trials in practice. We can also observe from the simulation studies that, the proposed procedure brings together the advantages of both techniques, in dealing with power, total sample size and total failure numbers, while keeps the type I error. In addition, we illustrate the characteristics of the proposed procedure by redesigning a well-known clinical trial of maternal-infant HIV transmission.