Method comparison studies explore the agreement of measurements made by two or more methods. Commonly, agreement is evaluated by the well-established Bland-Altman analysis. However, the underlying assumption is that differences between measurements are identically distributed for all observational units and in all application settings. We introduce the concept of conditional method agreement and propose a respective modeling approach to alleviate this constraint. Therefore, the Bland-Altman analysis is embedded in the framework of recursive partitioning to explicitly define subgroups with heterogeneous agreement in dependence of covariates in an exploratory analysis. Three different modeling approaches, conditional inference trees with an appropriate transformation of the modeled differences (CTreeTrafo), distributional regression trees (DistTree), and model-based trees (MOB) are considered. The performance of these models is evaluated in terms of type-I error probability and power in several simulation studies. Further, the adjusted rand index (ARI) is used to quantify the models' ability to uncover given subgroups. An application example to real data of accelerometer device measurements is used to demonstrate the applicability. Additionally, a two-sample Bland-Altman test is proposed for exploratory or confirmatory hypothesis testing of differences in agreement between subgroups. Results indicate that all models were able to detect given subgroups with high accuracy as the sample size increased. Relevant covariates that may affect agreement could be detected in the application to accelerometer data. We conclude that conditional method agreement trees (COAT) enable the exploratory analysis of method agreement in dependence of covariates and the respective exploratory or confirmatory hypothesis testing of group differences. It is made publicly available through the R package coat.

翻译：方法比较研究探讨两种或多种方法测量结果的一致性。通常，一致性通过成熟的Bland-Altman分析进行评估。然而，其潜在假设是测量差异在所有观测单元和应用场景中具有相同的分布。我们引入条件方法一致性的概念，并提出相应的建模方法以缓解这一限制。为此，将Bland-Altman分析嵌入递归划分框架中，在探索性分析中明确划分出依赖协变量的异质性一致性亚组。考虑了三种不同的建模方法：基于模型差异适当变换的条件推理树（CTreeTrafo）、分布回归树（DistTree）和基于模型的树（MOB）。通过多项模拟研究评估这些模型在第一类错误概率和统计功效方面的性能。此外，采用调整兰德指数（ARI）量化模型发现给定亚组的能力。应用加速度计设备测量数据的实例展示了方法的实用性。同时，提出两样本Bland-Altman检验，用于亚组间一致性的探索性或验证性假设检验。结果表明，随着样本量增加，所有模型均能以较高准确率检测出给定亚组。在加速度计数据应用中，检测到了可能影响一致性的相关协变量。我们得出结论：条件方法一致性树（COAT）能够实现依赖协变量的方法一致性的探索性分析，以及相应的亚组差异探索性或验证性假设检验。该方法已通过R包coat公开发布。