ABSTRACT

Benefit-risk assessment plays an important role in the evaluation of medical devices. Unlike the therapeutic devices, the diagnostic tests usually affect patient life indirectly since subsequent therapeutic treatment interventions (such as proper treatment in time, further examination or test, no action, etc.) will depend on correct diagnosis and monitoring of the disease status. A benefit-risk score using statistical models by integrating the information from benefit (true positive and true negative) and risk (false positive and false negative) for diagnostic tests with binary outcomes (i.e., positive and negative) will help evaluation of the utility and the uncertainty of a particular diagnostic device. In this paper, we develop two types of Bayesian models with conjugate priors for constructing the benefit-risk (BR) measures with corresponding credible intervals, one based on a Multinomial model with Dirichlet prior, and the other based on independent Binomial models with independent Beta priors. We then propose a Bayesian power prior model to incorporate the historical data or the real-world data (RWD). Both the fixed and random power prior parameters are considered for Bayesian borrowing. We evaluate the performance of the methods by simulations and illustrate their implementation using a real example.

摘要

利益-风险评估在医疗器械评估中发挥着重要作用。与治疗设备不同，诊断测试通常会间接影响患者的生活，因为后续的治疗干预（如及时正确治疗、进一步检查或测试、不采取行动等）将取决于对疾病状态的正确诊断和监测。通过整合来自具有二元结果（即阳性和阴性）的诊断测试的效益（真阳性和真阴性）和风险（假阳性和假阴性）的信息，使用统计模型的效益-风险评分将有助于评估效用和特定诊断设备的不确定性。在本文中，我们开发了两种具有共轭先验的贝叶斯模型，用于构建具有相应可信区间的收益-风险 (BR) 度量，一种基于具有 Dirichlet 先验的多项式模型，另一种基于具有独立 Beta 先验的独立二项式模型。然后，我们提出了一个贝叶斯幂先验模型来合并历史数据或现实世界数据 (RWD)。贝叶斯借用考虑了固定和随机功率先验参数。我们通过模拟来评估这些方法的性能，并使用一个真实的例子来说明它们的实现。然后，我们提出了一个贝叶斯幂先验模型来合并历史数据或现实世界数据 (RWD)。贝叶斯借用考虑了固定和随机功率先验参数。我们通过模拟来评估这些方法的性能，并使用一个真实的例子来说明它们的实现。然后，我们提出了一个贝叶斯幂先验模型来合并历史数据或现实世界数据 (RWD)。贝叶斯借用考虑了固定和随机功率先验参数。我们通过模拟来评估这些方法的性能，并使用一个真实的例子来说明它们的实现。