Robust multiple method comparison and transformation

TL;DR

This paper introduces a robust, generalized method for comparing multiple measurement techniques simultaneously, extending Passing-Bablok regression, with applications in clinical and laboratory settings, and includes visualization tools for variance analysis.

Contribution

It develops a new robust multi-method comparison approach based on median axes, generalizing Passing-Bablok regression for multiple methods, with practical applications and visualization tools.

Findings

Successfully applied to compare SARS-CoV-2 serological tests

Effective in interlaboratory trials and assay migration studies

Provides variance structure plots similar to Bland-Altman plots

Abstract

A generalization of Passing-Bablok regression is proposed for comparing multiple measurement methods simultaneously. Possible applications include assay migration studies or interlaboratory trials. When comparing only two methods, the method reduces to the usual Passing-Bablok estimator. It is close in spirit to reduced major axis regression, which is, however, not robust. To obtain a robust estimator, the major axis is replaced by the (hyper-)spherical median axis. The method is shown to reduce to the usual Passing-Bablok estimator if only two methods are compared. This technique has been applied to compare SARS-CoV-2 serological tests, bilirubin in neonates, and an in vitro diagnostic test using different instruments, sample preparations, and reagent lots. In addition, plots similar to the well-known Bland-Altman plots have been developed to represent the variance structure.