论文标题
多臂匪徒在剂量调查临床试验中的模型辅助设计中的应用
Application of Multi-Armed Bandits to Model-assisted designs for Dose-Finding Clinical Trials
论文作者
论文摘要
我们考虑将多军匪徒应用于模型辅助设计，以进行剂量调查临床试验。多臂匪徒是非常简单且强大的方法，可以确定动作以最大程度地提高奖励。在多臂匪徒中，我们首先考虑使用汤普森采样，该采样决定基于后验分布的随机样品的动作。在小样本量中，如剂量调查试验所示，因为后验分布的尾巴较重，随机样品的变异性太大，我们还考虑了正则化的汤普森采样和贪婪算法的应用。贪婪算法根据后平均值确定剂量。此外，我们还提出了一种基于后中间的剂量来确定剂量的方法。我们通过仿真研究评估了六种情况的拟议设计的性能。
We consider applying multi-armed bandits to model-assisted designs for dose-finding clinical trials. Multi-armed bandits are very simple and powerful methods to determine actions to maximize a reward in a limited number of trials. Among the multi-armed bandits, we first consider the use of Thompson sampling which determines actions based on random samples from a posterior distribution. In the small sample size, as shown in dose-finding trials, because the tails of posterior distribution are heavier and random samples are too much variability, we also consider an application of regularized Thompson sampling and greedy algorithm. The greedy algorithm determines a dose based on a posterior mean. In addition, we also propose a method to determine a dose based on a posterior median. We evaluate the performance of our proposed designs for six scenarios via simulation studies.
