Method comparison studies are concerned with estimating relationship between two clinical measurement methods. The methods often exhibit a structural change in the relationship over the measurement range. Ignoring this change would lead to an inaccurate estimate of the relationship. Motivated by a study of two digoxin assays where such a change occurs, this article develops a statistical methodology for appropriately analyzing such studies. Specifically, it proposes a segmented extension of the classical measurement error model to allow a piecewise linear relationship between the methods. The changepoint at which the transition takes place is treated as an unknown parameter in the model. An expectation‐maximization‐type algorithm is developed to fit the model and appropriate extensions of the existing measures are proposed for segment‐specific evaluation of similarity and agreement. Bootstrapping and large‐sample theory of maximum likelihood estimators are employed to perform the relevant inferences. The proposed methodology is evaluated by simulation and is illustrated by analyzing the digoxin data.

中文翻译：

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用于对方法比较数据进行建模和分析的分段测量误差模型。

方法比较研究涉及估计两种临床测量方法之间的关系。这些方法通常会在整个测量范围内显示出关系上的结构变化。忽略此更改将导致对关系的错误估计。出于研究发生这种变化的两种地高辛分析的动机，本文开发了一种统计方法，用于适当地分析此类研究。具体而言，它提出了经典测量误差模型的分段扩展，以允许方法之间的分段线性关系。转换发生的更改点在模型中被视为未知参数。开发了期望最大化类型算法以适合模型，并提出了现有度量的适当扩展，以用于针对特定段的相似性和一致性评估。采用自举和最大似然估计的大样本理论来执行相关的推论。通过仿真评估提出的方法，并通过分析地高辛数据进行说明。