何时重复生物标志物检测？从条件性重复测量中分解变异来源
When to repeat a biomarker test? Decomposing sources of variation from conditionally repeated measurements
AI总结：
本文提出贝叶斯分层框架，用于分解条件性重复测量中的变异，以估计个体误分类风险并指导基于证据的重新检测决策。
摘要：
基于初始结果重复进行不完美的生物标志物检测可能会引入偏倚并影响误分类风险。例如，在某些献血设置中，当初始测量低于最低阈值时，献血者的血红蛋白会再次测量。本文探讨了利用来自具有条件性重复生物标志物测量过程的数据，将连续生物标志物观测测量的变异分解为总体变异和测量程序变异的方法。我们提出了两种频率学方法，具有解析解，但这些方法在条件性重复献血者血红蛋白数据集中表现不佳，其中正常性假设不成立。然后我们开发了一个贝叶斯分层框架，允许不同的分布假设，我们将其应用于献血者血红蛋白数据集。使用假设总体血红蛋白服从正态分布，测量变异服从重尾t分布的贝叶斯分层模型，我们发现总测量变异占女性总方差的22%，男性占25%，女性献血者的总体标准差为1.07 g/dL，男性为1.28 g/dL。我们的贝叶斯框架可以利用任何具有条件性重复生物标志物测量的临床过程的数据，估计个体在一次或多次噪声连续测量后的误分类风险，并提供基于证据的条件重新检测决策规则。
英文摘要：
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.
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