Designing optimal, Bayesian decision-theoretic trials has traditionally required the use of computationally-intensive backward induction. While methods for addressing this barrier have been put forward, few are both computationally tractable and non-myopic, with applications of the Gittins index being one notable example. Here we explore the look-ahead approach with adaptive-randomization, with designs ranging from the fully myopic to the fully informed. We compare the operating characteristics of the look-ahead designed trials, in which decision rules are based on a fixed number of future blocks, with those of trials designed using traditional backward induction. The less-myopic designs performed well. As the designs become more myopic or the trials longer, there were disparities in regions of the decision space that are transition zones between continuation and stopping decisions. The more myopic trials generally suffered from early stopping as compared to the less myopic and backward induction trials. Myopic trials with adaptive randomization also saw as many as 28 % of their continuation decisions change to a different randomization ratio as compared to the backward induction designs. Finally, early stages of myopic-designed trials may have disproportionate effect on trial characteristics.

中文翻译：

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在贝叶斯自适应随机决策理论临床试验设计中，固定水平、前瞻程序与反向归纳的性能比较

设计最优的贝叶斯决策理论试验传统上需要使用计算密集型的反向归纳。虽然已经提出了解决这一障碍的方法，但很少有计算上易于处理和非短视的方法，Gittins 指数的应用就是一个显着的例子。在这里，我们探索了具有自适应随机化的前瞻性方法，其设计范围从完全近视到完全知情。我们比较了前瞻设计试验的操作特征，其中决策规则基于固定数量的未来块，与使用传统反向归纳设计的试验的操作特征进行比较。不那么近视的设计表现良好。随着设计变得更加近视或试验时间更长，作为继续和停止决策之间的过渡区域的决策空间区域存在差异。与较少近视和反向诱导试验相比，更多近视试验通常会提前停止。与反向诱导设计相比，具有自适应随机化的近视试验也发现多达 28% 的继续决策变为不同的随机化比率。最后，近视设计试验的早期阶段可能对试验特征产生不成比例的影响。与反向诱导设计相比，具有自适应随机化的近视试验也发现多达 28% 的继续决策变为不同的随机化比率。最后，近视设计试验的早期阶段可能对试验特征产生不成比例的影响。与反向诱导设计相比，具有自适应随机化的近视试验也发现多达 28% 的继续决策变为不同的随机化比率。最后，近视设计试验的早期阶段可能对试验特征产生不成比例的影响。