Inferential Validity of Digital Health Measures

Abstract

Digital health measures increasingly inform treatment evaluation, risk classification, clinical monitoring, and regulatory decisions. Existing validity concepts concern a measure's technical soundness, clinical meaningfulness, usability, and scalability. Inferential validity concerns how well a measure supports a scientific, clinical, or regulatory conclusion. We propose assessing inferential validity as a downstream evaluation layer that links measurement system evidence to trustworthy conclusions.