Stochastic approximation with virtual observations for dose-finding on discrete levels.
Stochastic approximation with virtual observations for dose-finding on discrete levels.
复制标题
具有虚拟观察的随机近似，用于离散水平上的剂量查找。
DOI：
10.1093/biomet/asp065
复制
发表时间：
2010
期刊：
影响因子：
2.7
通讯作者：
Elkind,MitchellSV
中科院分区：
文献类型：
--
作者：
Cheung,YingKuen;Elkind,MitchellSV
I期临床研究是将一种新药施用于人体，以确定在目标概率下引起毒性的最大剂量的实验。I期剂量探索通常被表述为分位数估计问题。对于具有生物学终点的研究，通常通过对连续生物标志物表达进行二分来定义毒性。在这篇文章中，我们提出了一个新的变种的Robbins-Monro随机近似，利用连续测量分位数估计。Robbins-Monro方法很少见到临床应用，因为它在二进制数据的分位数估计方面表现不佳，并且它适用于实践中通常不可用的连续剂量。为了解决这些问题，我们制定的剂量寻找问题的连续变量的平均值，随机逼近过程是有效的根查找。为了适应离散剂量的使用，我们引入了在连续剂量范围上定义的虚拟观察的想法。我们提出的方法继承了随机近似算法的收敛性和计算简单性。基于真实的试验数据的仿真结果表明，我们提出的方法提高了准确性相比，不断重新评估方法和产生的结果鲁棒模型误设定。
Phase I clinical studies are experiments in which a new drug is administered to humans to determine the maximum dose that causes toxicity with a target probability. Phase I dose-finding is often formulated as a quantile estimation problem. For studies with a biological endpoint, it is common to define toxicity by dichotomizing the continuous biomarker expression. In this article, we propose a novel variant of the Robbins–Monro stochastic approximation that utilizes the continuous measurements for quantile estimation. The Robbins–Monro method has seldom seen clinical applications, because it does not perform well for quantile estimation with binary data and it works with a continuum of doses that are generally not available in practice. To address these issues, we formulate the dose-finding problem as root-finding for the mean of a continuous variable, for which the stochastic approximation procedure is efficient. To accommodate the use of discrete doses, we introduce the idea of virtual observation that is defined on a continuous dosage range. Our proposed method inherits the convergence properties of the stochastic approximation algorithm and its computational simplicity. Simulations based on real trial data show that our proposed method improves accuracy compared with the continual re-assessment method and produces results robust to model misspecification.