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.
Keywords: Binary diagnostic test; benefit-risk assessment; bootstrap; delta method.