USING THE NET MONETARY BENEFIT FRAMEWORK FOR THE OPTIMAL DICHOTOMIZATION OF DIAGNOSTIC TESTS: A CASE STUDY OF A DYSGLYCEMIA SCREENING PROGRAM

Purpose: Cut-off points on diagnostic tests are almost always chosen without considering for the costs and health effects resulting from that decision. This is remarkable, seeing that the treatment strategies following a test result are increasingly judged on their cost-effectiveness. To address this issue, we discuss how an optimization objective can be formulated that allows identifying an optimal cut-off point by taking into account all relevant downstream costs and health effects.

Method: The Net Monetary Benefit (NMB) framework is widely applied healthcare in evaluation and familiar to decision makers. Maximizing NMB is therefore a suitable decision objective. Based on the clinical setting in which the diagnostic test is used and the clinical actions that will be based on the test outcome, the parameters of the NMB equation can be expressed as functions of the cut-off on the test. By maximizing this function the cut-off resulting in the highest NMB can be found. We illustrate this method by means of a case study of a dysglycemia screening program, where a questionnaire is used to identify those with a high probability of having dysglycemia. In this case study, we use data from a prospective cohort study to estimate the fraction of patients enrolled in treatment and screening costs for all possible cut-off values. We use data from literature to treatment costs and effects. We compared our method to the Youden index, which is the most widely used conventional approach for selecting a cut-off.

Result: The NMB of the cut-off found with our method was 15% higher than that of the cut-off found with the Youden index. Additionally, changes in factors related to the implementation setting of the test (patient non-response, variability in costs of diagnostics) had a profound impact on which cut-off led to the highest NMB. Such factors cannot be incorporated in the decision using the Youden index or other conventional approaches.

Conclusion: The current methods for selecting a cut-off on a diagnostic test do not lead to a cut-off that is regarded optimal in terms of the allocation of scarce healthcare resources and the health effects and are not suited to incorporate all real-life aspects that influence the results of the cut-off decision.