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.

翻译：基于初始结果重复进行不完美的生物标志物检测可能引入偏倚并影响误分类风险。例如，在某些献血场景中，当献血者的初始血红蛋白测量值低于献血资格最低阈值时，会对其进行重新测量。本文探讨了利用条件性重复生物标志物测量过程所产生的数据，将连续生物标志物观测测量值的变异分解为群体变异与测量程序引起的变异的方法。我们提出了两种具有解析解的频率学派方法，但这些方法在正态性假设不满足的条件性重复献血者血红蛋白测量数据集中表现不佳。随后我们开发了一个允许不同分布假设的贝叶斯分层框架，并将其应用于献血者血红蛋白数据集。通过采用假设群体血红蛋白呈正态分布且测量变异服从重尾$t$分布的贝叶斯分层模型，我们发现测量总变异在女性中占总方差的22%，在男性中占25%，女性献血者的群体标准差为$1.07\, \rm g/dL$，男性献血者为$1.28\, \rm g/dL$。我们的贝叶斯框架可利用任何具有条件性重复生物标志物测量的临床过程所产生的数据，来估计个体在经历一次或多次含噪连续测量后的误分类风险，并为基于证据的条件性复测决策规则提供依据。