Beta-Binomial Statistical Model for Validation Studies of Analytes with a Binary Response.

Abstract

Background: The probability of detection (POD) model has had widespread application for statistically analyzing single and multiple collaborator validations studies with binary outcome data for a wide range of analytes over the last decade.

Objective: The POD model is placed on a firm theoretical foundation, and extended to a more generalized beta-binomial model.

Methods: The POD model is revisited and embedded in the beta-binomial model. This generalization includes collaborator reproducibility as a specific parameter. The new model includes only two distributional parameters: the overall across-collaborator probability of detection (LPOD) and the intraclass correlation of collaborators (ICC), measuring irreproducibility. Differences between methods are measured by the difference in LPOD values, denoted dLPOD.

Results: Accurate statistical estimators and confidence intervals are provided with validation by simulation. This new beta-binomial model will be applicable to a full range of candidate methods giving binary qualitative results, including microbiological, toxin, allergen, biothreat, and botanical analytes.

Conclusions: The new beta-binomial model provides easy equivalence tests to show the study clearly demonstrates (with 95% confidence) that the method differences and collaborator reproducibility are acceptable.

Highlights: The validation system for qualitative binary methods using probability of detection (POD) of an analyte as the parameter of interest has been modified and further validated.