Incorporation of stochastic engineering models as prior information in Bayesian medical device trials
Incorporation of stochastic engineering models as prior information in Bayesian medical device trials
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DOI：
10.1080/10543406.2017.1300907
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发表时间：
2017-01-01
影响因子：
1.1
通讯作者：
Nair, Rajesh
中科院分区：
文献类型：
--
作者：
Haddad, Tarek;Himes, Adam;Nair, Rajesh
在将新产品推向市场的过程中，通过临床试验对医疗器械进行评估通常是必要的步骤。近年来，设备制造商越来越多地在产品开发过程中使用随机工程模型。这些模型具有模拟虚拟患者结果的能力。本文提出了一种基于功率先验的利用虚拟患者数据扩充临床试验的新方法。为了适当地为临床评估提供信息，虚拟患者模型必须模拟感兴趣的临床结果，包括患者的变异性，以及工程模型及其输入参数中的不确定性。虚拟患者的数量由折扣函数控制，该函数使用建模数据和观测数据之间的相似性。以心脏导联骨折为例说明了该方法的有效性。不同的折扣函数用于覆盖多种情况，在这些情况下，对于相同数量的入选患者，类型I错误率和功率不同。在贝叶斯临床试验设计中结合工程模型作为先验知识，可以提供减少样本量和试验长度的好处，同时仍然控制I型错误率和功率。
Evaluation of medical devices via clinical trial is often a necessary step in the process of bringing a new product to market. In recent years, device manufacturers are increasingly using stochastic engineering models during the product development process. These models have the capability to simulate virtual patient outcomes. This article presents a novel method based on the power prior for augmenting a clinical trial using virtual patient data. To properly inform clinical evaluation, the virtual patient model must simulate the clinical outcome of interest, incorporating patient variability, as well as the uncertainty in the engineering model and in its input parameters. The number of virtual patients is controlled by a discount function which uses the similarity between modeled and observed data. This method is illustrated by a case study of cardiac lead fracture. Different discount functions are used to cover a wide range of scenarios in which the type I error rates and power vary for the same number of enrolled patients. Incorporation of engineering models as prior knowledge in a Bayesian clinical trial design can provide benefits of decreased sample size and trial length while still controlling type I error rate and power.