In diagnostic device evaluation, it is important to have an integrated benefit-risk (BR) assessment for safety and effectiveness, which is not same as the assessment for drugs and therapeutic devices. Correct diagnosis does not lead to direct clinical outcome such as longer survival, release of symptoms, tumor shrinkage, etc.; but leads to the proper treatment in time while incorrect diagnosis may result in serious consequences of unnecessary tests and wrong treatments. Some common measures used in evaluating the accuracy of a diagnostic device include sensitivity, specificity, positive predictive value and negative predictive value. Here, we propose a BR measure by incorporating information about true-positive and true-negative cases (correct diagnosis) and false-positive and false-negative cases (incorrect diagnosis) for facilitating the necessary decision-making. Three decision rules are discussed depending on the purpose of the clinical study. Different statistical models are developed for estimating the BR measure for data obtained from different sampling schemes (cross-sectional and case–control sampling). The construction of confidence intervals (CIs) for the proposed BR measure is based on (i) the asymptotic normality of the maximum likelihood estimators (MLEs), and (ii) parametric bootstrap re-sampling technique. The performance of these CIs is evaluated by intensive Monte-Carlo simulations which reveal that both CIs perform reasonably well. Finally, the proposed methodology is applied to two clinical trial datasets.

在诊断设备评估中，重要的是要进行安全性和有效性的综合利益风险（BR）评估，这与药物和治疗设备的评估不同。正确的诊断不会导致直接的临床结果，例如更长的生存期，症状的缓解，肿瘤缩小等；但是会导致及时的适当治疗，而错误的诊断可能会导致不必要的测试和错误治疗的严重后果。用于评估诊断设备准确性的一些常用措施包括敏感性，特异性，阳性预测值和阴性预测值。这里，我们通过结合有关真实阳性和真实阴性病例（正确诊断）以及错误阳性和假阴性病例（错误诊断）的信息来提出BR措施，以促进必要的决策。根据临床研究的目的，讨论了三个决策规则。开发了不同的统计模型来估计从不同抽样方案（横截面抽样和病例对照抽样）获得的数据的BR度量。所建议的BR度量的置信区间（CIs）的构建基于（i）最大似然估计器（MLE）的渐近正态性，和（ii）参数自举重采样技术。这些CI的性能通过密集的蒙特卡洛模拟评估，表明两个CI的性能都相当好。最后，