The Bland‐Altman method, which assesses agreement via an assessment set constructed by the difference of the measurement variables, has received great attention. Other assessment approaches have been proposed following the same difference‐based framework. However, the exact assessment set constructed by the difference is achievable only for measurements with certain joint distributions. To provide a more general assessment framework, we propose two approaches. First, when the measurement distribution is known, we propose a parametric approach that constructs the assessment set through a measure of closeness corresponding to the distribution. Second, when the measurement distribution is unknown, we propose a nonparametric approach that constructs the assessment set through quantile regression. Both approaches quantify the degree of agreement with the presence of both systematic and random measurement errors, and enable one to go beyond the difference‐based approach. Results of simulation and data analyses are presented to compare the two approaches.

中文翻译：

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布兰德和奥特曼对协议方法的评估中的参数和非参数改进

Bland-Altman方法通过由测量变量的差异构成的评估集评估一致性，因此受到了极大的关注。遵循相同的基于差异的框架，还提出了其他评估方法。但是，由差异构成的精确评估集仅可用于具有特定关节分布的测量。为了提供一个更通用的评估框架，我们提出了两种方法。首先，在已知测量分布的情况下，我们提出了一种参数化方法，该方法通过对应于分布的接近度度量来构建评估集。其次，当度量分布未知时，我们提出了一种非参数方法，该方法通过分位数回归来构建评估集。两种方法都可以量化存在系统误差和随机测量误差的一致性程度，并使一种方法能够超越基于差异的方法。给出了仿真和数据分析的结果，以比较这两种方法。