Title: Bayesian adaptive N-of-1 trials for estimating population and individual treatment effects Show Chinese title

Title: 贝叶斯自适应N-of-1试验用于估计群体和个体治疗效果

Abstract: 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 to the multi-armed bandit approach and a randomised 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. Show Chinese abstract

Abstract: 本文提出了一种新颖的自适应设计算法，可用于寻找 N-of-1 临床试验中的最优治疗分配。 该新方法使用两次拉普拉斯近似，在重复测量的自适应设计框架内提供人群和个体随机效应的计算高效估计。 鉴于此方法的效率，它也被用于治疗选择，以针对收集数据以精确估计治疗效果。 为了评估这种方法，我们考虑了文献中一个模拟的和激励性的 N-of-1 临床试验。 对于每个试验，我们的方法与多臂老虎机方法和随机化 N-of-1 试验设计在识别每位患者的最佳治疗以及关于模型参数所获得的信息方面进行了比较。 结果显示，我们提出的新方法选择了在实现这些目标方面非常高效的试验设计。 因此，我们建议基于拉普拉斯的算法作为设计自适应 N-of-1 试验的一种高效方法。