AI 中文总结
研究针对多仪器方法比较问题，基于精度加权戴明回归进行扩展，开发了用于拟合、形式推断、残差分析及异常值检测与诊断的算法，以处理测量变异性随分析物值变化的情况。
AI 中文摘要
在方法比较（MC）研究中，使用两种或更多仪器测试样本，以建立不同仪器读数间的统计关系。这与常规回归不同，是变量误差问题。关系可参数化拟合（戴明回归）或非参数化拟合（Passing Bablok或PB回归）。在临床化学环境中，测量变异性很少恒定，通常随分析物值增加而增大。精度加权戴明回归对这种变异性建模并纳入拟合。本文将双仪器戴明模型扩展到多仪器，开发了拟合、形式推断、残差分析以及异常值检测与诊断的算法。
英文摘要
In methods comparison (MC) studies, specimens are tested using two or more instruments with the objective of establishing the statistical relationship between the different instruments readings. Unlike regular regression, this is an errors in variables problem. Relationships may be fitted parametrically (Deming regression) or non-parametrically (Passing Bablok or PB regression.) In clinical chemistry settings, the measurement variability is rarely constant, but generally increases with increasing analyte values. Precision weighted Deming regression models this variability and incorporates it into the fitting. The simplest setting of comparing two instruments is discussed in (1) and implemented in an R package (2). PB makes minimal distributional assumptions. Its classical two-instrument implementation has recently been extended to multiple instruments (3). This work extends the two-instrument Deming model of (1) to multiple instruments, developing algorithms for fitting, for formal inference, for residual analysis, and for outlier detection and diagnosis.
Comments27 pages 12 figures
