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Background Clinical pathways with multiple diagnostic tests are complex to model, but problematic and simplistic approaches are often used in economic evaluations. Methods We analyzed statistical methods of handling multiple diagnostic tests and provided guidance on applying these methods in economic modeling. We first introduced a statistical model to quantify the correlations between 2 tests and how those correlations can be incorporated within an economic model. We also presented the general form of conditional dependence among multiple tests. We then introduced net reclassification improvement (NRI), a measure that evaluates the added value of a new risk factor (e.g., biomarker) for risk prediction. We further provided 2 examples to illustrate the application of these methods. Results Our first example illustrated how to model an add-on test to an existing test, in the absence of a perfect reference standard. After accounting for the imperfect nature of both tests and the conditional dependence between tests, the potential health benefits from the additional test were reduced. This led to differential cost-effectiveness results when comparing models using the perfect test and conditional independence assumptions. The second example illustrated how to evaluate the added value of a new risk factor using the NRI measure. Using the new risk classification provides greater precision in risk prediction, and in the example, the strategy using the new risk classification with treatment for selected individuals led to more favorable cost-effectiveness results. Conclusions These innovative methods for handling multiple diagnostic tests have improved the methodology within the field and should be adopted to provide more accurate estimates within cost-effectiveness analyses. Highlights Economic evaluations of multiple diagnostic tests often apply problematic simplistic approaches, such as ignoring conditional dependence between 2 tests or assuming a perfect final test in the diagnostic pathway. We provided guidance on how to apply improved methods for economic modeling. We introduced methods to model conditional dependence between 2 imperfect tests. We used an example to illustrate how assumptions about perfect diagnostic test accuracy and conditional independence between tests affect cost-effectiveness. Compared with the results of the area under the receiver-operating-characteristic curve, net reclassification improvement has distinct advantages in measuring the added value of a new risk factor for model-based economic evaluation. Economic evaluations that appropriately account for the complexities of diagnostic test pathways can help decision makers ensure efficient use of resources.

About Us: The Ontario COVID-19 Science Advisory Table is a group of scientific experts and health system leaders who evaluate and report on emerging evidence relevant to the COVID-19 pandemic, to inform Ontario's response. Our mandate is to provide weekly summaries of relevant scientific evidence for the COVID-19 Health Coordination Table of the Province of Ontario, integrating information from existing scientific tables, Ontario's universities and agencies, and the best global evidence. The Science Table summarizes its findings for the Health Coordination Table and the public in Science Briefs. The Drugs & Biologics Clinical PracticeGuidelines Working Group is a group of clinicians and scientists with recognized expertise in drugs, biologics, and clinical care. The Working Group evaluates existing scientific data, disease epidemiology, drug availability, and implementation issues in order to develop Clinical Practice Guidelines for the treatment of COVID-19 using drugs and biologics. The Working Group reports its findings to the public and the Science Table . Its findings are also summarized in Science Briefs.

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