稳健的多方法比较与转换
Robust multiple method comparison and transformation
AI总结：
提出 Passing-Bablok 回归的稳健多方法推广，以（超）球形中轴替代主轴，可同时比较多种测量方法并配套类 Bland-Altman 图展示方差结构。
摘要：
本文提出了 Passing-Bablok 回归的一种推广，用于同时比较多种测量方法。可能的应用包括检测方法迁移研究或实验室间试验。当仅比较两种方法时，该方法退化为通常的 Passing-Bablok 估计量。其思想接近于约化主轴回归，但后者并不稳健。为获得稳健估计量，主轴被替换为（超）球形中轴。研究表明，若仅比较两种方法，该方法即退化为通常的 Passing-Bablok 估计量。该技术已被用于比较 SARS-CoV-2 血清学检测、新生儿胆红素，以及使用不同仪器、样本制备和试剂批次的体外诊断检测。此外，还开发了类似于著名 Bland-Altman 图的图形，用以表示方差结构。
英文摘要：
A generalization of Passing-Bablok regression is proposed for comparing multiple measurement methods simultaneously. Possible applications include assay migration studies or interlaboratory trials. When comparing only two methods, the method reduces to the usual Passing-Bablok estimator. It is close in spirit to reduced major axis regression, which is, however, not robust. To obtain a robust estimator, the major axis is replaced by the (hyper-)spherical median axis. The method is shown to reduce to the usual Passing-Bablok estimator if only two methods are compared. This technique has been applied to compare SARS-CoV-2 serological tests, bilirubin in neonates, and an in vitro diagnostic test using different instruments, sample preparations, and reagent lots. In addition, plots similar to the well-known Bland-Altman plots have been developed to represent the variance structure.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
