A crucial step in the design of a diagnostic test is determining the cutoff point, the threshold which separates a negative measurement from a positive one. The results of a diagnostic test have clinical consequences: only when disease is accurately detected, proper treatments be administered, and vice versa. Benefit-Risk (BR) analysis should be used to determine the optimal cutoff point that optimizes the consequence. Quantitative BR analysis requires measurable benefit and risk and a function, e.g., linear or ratio, to combine all the components. When BR corresponding to the four possible diagnostic test outcomes are all scaled in units of risk resulting from an untreated disease, we propose a net BR (linear BR) equation as a function of diagnostic parameters, disease prevalence, benefit of correct diagnosis and risk of false diagnostic results. Optimal cutoff of a diagnostic test can be obtained using this function. Comparison of diagnostic tests based on their benefit and risk of tests is also discussed. Use of this function is illustrated with a biosensor rapid antigen test for SARS-CoV-2.
Keywords: BR categories; BR functions; Comparison of diagnostic tests; Diagnostic test for SARS- CoV-2; Optimization of BR functions.
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