Data from: Assessing agreement with relative area under the coverage probability curve

There has been substantial statistical literature in the last several decades on assessing agreement and coverage probability approach was selected as a preferred index for assessing and improving measurement agreement in a core laboratory setting [1]. With this approach, a satisfactory agreement is based on pre-specified high satisfactory coverage probability (e.g., 95%), given one pre-specified acceptable difference. In practice, we may want to have quality control on more than one pre-specified differences or we may simply want to summarize the agreement based on differences up to a maximum acceptable difference. We propose to assess agreement via the coverage probability curve that provides a full spectrum of measurement error at various differences/disagreement. Relative area under the coverage probability curve is proposed for the summary of overall agreement and this new summary index can be used for comparison of different intra- or inter-methods/labs/observers’ agreement. Simulation studies and a blood pressure example are used for illustration of the methodology.<br>

近数十年来，测量一致性评估领域已积累了大量统计研究成果，其中覆盖概率（Coverage Probability）法被选为核心实验室场景下评估与优化测量一致性的优选指标[1]。采用该方法时，若给定单一预设可接受差值，则满意的测量一致性需满足预先设定的高合格覆盖概率（例如95%）。但在实际应用中，我们往往需要对多个预设差值开展质量控制，或是仅希望基于最大可接受差值以内的所有差值来综合总结测量一致性。为此，我们提出通过覆盖概率曲线来评估测量一致性，该曲线可完整呈现不同差值/不一致程度下的测量误差分布全貌。我们进一步提出将覆盖概率曲线下相对面积作为整体一致性的总结指标，这一新型总结指标可用于对比不同方法内、方法间、实验室间或观察者间的测量一致性水平。本文通过模拟研究与血压实测案例对所提方法进行了演示说明。