When to repeat a biomarker test? Decomposing sources of variation from conditionally repeated measurements

TL;DR

This paper develops a Bayesian hierarchical model to decompose variation sources in conditionally repeated biomarker measurements, improving understanding of measurement error and aiding retesting decision rules.

Contribution

It introduces a flexible Bayesian framework that accommodates various distributional assumptions for analyzing repeated biomarker data, outperforming traditional frequentist methods.

Findings

Measurement variation accounts for 22-25% of total variance among donors.

Population standard deviations are estimated at 1.07 g/dL for females and 1.28 g/dL for males.

The model helps estimate misclassification risk and guides retesting decisions.

Abstract

Repeating an imperfect biomarker test based on an initial result can introduce bias and influence misclassification risk. For example, in some blood donation settings, blood donors' hemoglobin is remeasured when the initial measurement falls below a minimum threshold for donor eligibility. This paper explores methods that use data resulting from processes with conditionally repeated biomarker measurement to decompose the variation in observed measurements of a continuous biomarker into population variability and variability arising from the measurement procedure. We present two frequentist approaches with analytical solutions, but these approaches perform poorly in a dataset of conditionally repeated blood donor hemoglobin measurements where normality assumptions are not met. We then develop a Bayesian hierarchical framework that allows for different distributional assumptions, which we apply to the blood donor hemoglobin dataset. Using a Bayesian hierarchical model that assumes normally distributed population hemoglobin and heavy tailed $t$-distributed measurement variation, we found that the total measurement variation accounted for 22\% of the total variance among females and 25\% among males, with population standard deviations of $1.07\, \rm g/dL$ for female donors and $1.28\, \rm g/dL$ for male donors. Our Bayesian framework can use data resulting from any clinical process with conditionally repeated biomarker measurements to estimate individuals' misclassification risk after one or more noisy continuous measurements and inform evidence-based conditional retesting decision rules.