This article proposes a novel adaptive design algorithm that can be used to find optimal treatment allocations in N‐of‐1 clinical trials. This new methodology uses two Laplace approximations to provide a computationally efficient estimate of population and individual random effects within a repeated measures, adaptive design framework. Given the efficiency of this approach, it is also adopted for treatment selection to target the collection of data for the precise estimation of treatment effects. To evaluate this approach, we consider both a simulated and motivating N‐of‐1 clinical trial from the literature. For each trial, our methods were compared with the multiarmed bandit approach and a randomized N‐of‐1 trial design in terms of identifying the best treatment for each patient and the information gained about the model parameters. The results show that our new approach selects designs that are highly efficient in achieving each of these objectives. As such, we propose our Laplace‐based algorithm as an efficient approach for designing adaptive N‐of‐1 trials.

中文翻译：

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贝叶斯自适应N-of-1试验，用于估计人群和个体治疗效果。

本文提出了一种新颖的自适应设计算法，该算法可用于在N分之1的临床试验中找到最佳治疗方案。这种新方法使用两个拉普拉斯近似值，以在重复测量，自适应设计框架内提供总体和个体随机效应的计算有效估计。考虑到这种方法的效率，还可以将其用于治疗选择，将数据收集作为目标以精确估计治疗效果。为了评估这种方法，我们考虑了来自文献的模拟和激励性N-1的临床试验。在每项试验中，我们的方法均与多臂匪徒方法和随机N-1的试验设计进行了比较，以确定每个患者的最佳治疗方法以及有关模型参数的信息。结果表明，我们的新方法选择了可以高效实现这些目标的设计。因此，我们提出基于Laplace的算法作为设计自适应N-1试验的一种有效方法。