Background: In clinical research, the Bland-Altman analysis is commonly used to assess agreement of metric measurements made by two or more techniques, devices or methods. The approach can also deal with repeated measurements per subject or observational unit. However, a strong and implicit assumption is that agreement of methods is homogeneous across subjects. Objective: To extend the previously introduced multivariable modeling of conditional method agreement with single measurements per subject to the frequent case of repeated measurements. Methods: Appropriate regression trees, called conditional method agreement trees (COAT), are generalized to capture the dependence of the parameters of the Bland-Altman analysis on covariates. These parameters, the expectation and variance of the differences between the methods, are decomposed into subject-specific components to account for repeated measurements. Whilst the theoretical, asymptotic properties of tree models are known, a simulation study was carried out to assess the performance of COAT in finite samples. A comparison of devices measuring cardiac output serves as an application example. Results: COAT is applicable to the two relevant cases of paired and unpaired repeated measurements. In the simulation study, it controlled the type-I error at the nominal level and could detect covariate-dependent method agreement with increasing sample size. The Adjusted Rand Index, a measure of concordance between the estimated and true subgroups, reached very high values close to the maximum of 1. The analysis of cardiac output showed that patients' characteristics may influence the agreement between measuring devices, with implications for use in patient care. Conclusion: COAT can explicitly define subgroups of heterogeneous method agreement in dependence of covariates with appropriate statistical testing in case of repeated measurements.

翻译：背景：在临床研究中，Bland-Altman分析常被用于评估两种或多种技术、设备或方法所得计量测量值的一致性。该方法亦可处理每个受试者或观察单位存在重复测量的情况，但其隐含的强假设是各方法间的一致性在受试者间具有同质性。目的：将先前提出的针对每个受试者单次测量的条件性方法一致性多变量建模方法扩展至重复测量的常见场景。方法：通过推广名为条件性方法一致性树（COAT）的适当回归树模型，捕捉Bland-Altman分析参数对协变量的依赖性。通过将方法间差异的期望与方差分解为受试者特异性成分，以处理重复测量数据。尽管树模型的理论渐近性质已知，本研究仍通过模拟实验评估有限样本下COAT的性能，并以心脏输出量测量设备的比较作为应用实例。结果：COAT适用于配对与非配对重复测量两种相关情形。模拟实验表明，该模型在名义显著性水平下控制了一类错误，并能随样本量增加检测出协变量依赖的方法一致性差异。调整后兰德指数（衡量估计与真实亚组一致性的指标）达到接近最大值1的极高数值。心脏输出量分析显示，患者特征可能影响测量设备间的一致性，这对患者护理中的设备使用具有重要启示。结论：COAT可通过适当的统计检验，在重复测量场景下依据协变量显式定义方法一致性存在异质性的亚组。