CONTEST: A generalization of ONEST to estimate sample size for predictive augmented intelligence method validation studies.

ABSTRACT: Laboratories must verify and validate assays before reporting results in the clinical record. With the advent of machine learning algorithms, multiclass decision-support tools are coming online but the FDA explicitly does not contemplate multiclass problems in their guidance for test validation. Validation requires, for a laboratory's patient population, evaluation of four performance characteristics to a reference method: accuracy, precision, reportable range, and reference intervals. In the absence of a reference method, proportion of agreement is the appropriate metric (Meier 2007). For subjective tests, the traditional metrics for precision are in the area of interrater reliability, and interrater reliability is well studied in the pathology literature ("Gwet Handbook of Interrater Rel

SUBMITTER: Olson BK 

PROVIDER: S-EPMC12637083 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature