Volta

A framework for adaptive predictive values using individual symptoms of screening test users

Abstract Aim Screening tests are widely used for disease detection, but test users often struggle to interpret negative or positive test results correctly, in particular in the presence of symptoms. This study proposes a framework for adaptive predictive values (APV) that personalises test interpretation by incorporating individual symptoms. Subject and methods The APV framework integrates individual symptom data into the calculation of predictive values, modifying the individual’s prior probability of being diseased. The discriminatory power of different symptoms can be determined via Bayes Factors. The framework is illustrated by a web application (ShinyApp) for the estimation of predictive values for SARS-CoV-2 infection based on the presence of typical symptoms, by reusing the symptom profile data from the REACT-1 study, which …

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