Optimal adaptive randomized designs for clinical trials
临床试验的最优自适应随机设计AI 解读 ↓
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AI 根据所提供的论文材料归类：摘要直接研究治疗随机分配、自适应试验设计与分配偏差，属于因果研究的实验设计方向。
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临床试验的最优自适应随机设计
- 研究问题
- 如何在多臂序贯临床试验中兼顾随机分配与患者效用，既限制分配偏差，又减少患者进入表现较差治疗组的数量？
- 方法与思路
- 作者为每个治疗臂设置最低分配概率，在贝叶斯决策框架下最大化患者期界内的期望总效用，并用逆向递推进行计算。
- 材料陈述的贡献
- 摘要称证明受约束最优设计的分配概率结构及特定极限下的渐近最优性；两臂计算还展示较差治疗组的分配减少。
- 阅读关注点
- 应核对效用、患者期界与试验样本量的定义、最低概率约束，以及渐近结论和相同功效比较分别依赖哪些条件。
- 阅读边界
- 仅据版本未注明的 OpenAlex 索引摘要；理论结论有特定极限条件，数值展示主要为两臂，不能直接外推多臂表现或刊本变化。
原文摘要
Optimal decision-analytic designs are deterministic. Such designs are appropriately criticized in the context of clinical trials because they are subject to assignment bias. On the other hand, balanced randomized designs may assign an excessive number of patients to a treatment arm that is performing relatively poorly. We propose a compromise between these two extremes, one that achieves some of the good characteristics of both. We introduce a constrained optimal adaptive design for a fully sequential randomized clinical trial with k arms and n patients. An r-design is one for which, at each allocation, each arm has probability at least r of being chosen, 0 ⩽ r ⩽ 1/k. An optimal design among all r-designs is called r-optimal. An r1-design is also an r2-design if r1 ⩾ r2. A design without constraint is the special case r = 0 and a balanced randomized design is the special case r = 1/k. The optimization criterion is to maximize the expected overall utility in a Bayesian decision-analytic approach, where utility is the sum over the utilities for individual patients over a ‘patient horizon’ N. We prove analytically that there exists an r-optimal design such that each patient is assigned to a particular one of the arms with probability 1 − (k − 1)r, and to the remaining arms with probability r. We also show that the balanced design is asymptotically r-optimal for any given r, 0 ⩽ r < 1/k, as N/n → ∞. This implies that every r-optimal design is asymptotically optimal without constraint. Numerical computations using backward induction for k = 2 arms show that, in general, this asymptotic optimality feature for r-optimal designs can be accomplished with moderate trial size n if the patient horizon N is large relative to n. We also show that, in a trial with an r-optimal design, r < 1/2, fewer patients are assigned to an inferior arm than when following a balanced design, even for r-optimal designs having the same statistical power as a balanced design. We discuss extensions to various clinical trial settings.