Flinders University International Centre for Point-of-Care Testing, Flinders Health and Medical Research Institute, Bedford Park, Australia.
Engineering Cluster, Singapore Institute of Technology, Singapore, Singapore.
Clin Chem Lab Med. 2022 Jun 1;60(8):1164-1174. doi: 10.1515/cclm-2022-0205. Print 2022 Jul 26.
One approach to assessing reference material (RM) commutability and agreement with clinical samples (CS) is to use ordinary least squares or Deming regression with prediction intervals. This approach assumes constant variance that may not be fulfilled by the measurement procedures. Flexible regression frameworks which relax this assumption, such as quantile regression or generalized additive models for location, scale, and shape (GAMLSS), have recently been implemented, which can model the changing variance with measurand concentration.
We simulated four imprecision profiles, ranging from simple constant variance to complex mixtures of constant and proportional variance, and examined the effects on commutability assessment outcomes with above four regression frameworks and varying the number of CS, data transformations and RM location relative to CS concentration. Regression framework performance was determined by the proportion of false rejections of commutability from prediction intervals or centiles across relative RM concentrations and was compared with the expected nominal probability coverage.
In simple variance profiles (constant or proportional variance), Deming regression, without or with logarithmic transformation respectively, is the most efficient approach. In mixed variance profiles, GAMLSS with smoothing techniques are more appropriate, with consideration given to increasing the number of CS and the relative location of RM. In the case where analytical coefficients of variation profiles are U-shaped, even the more flexible regression frameworks may not be entirely suitable.
In commutability assessments, variance profiles of measurement procedures and location of RM in respect to clinical sample concentration significantly influence the false rejection rate of commutability.
评估参考物质（RM）与临床样本（CS）的可互换性和一致性的一种方法是使用普通最小二乘法或带有预测区间的德明回归。这种方法假设方差是恒定的，但测量程序可能无法满足这一假设。最近已经实施了一些灵活的回归框架，例如分位数回归或位置、比例和形状的广义加性模型（GAMLSS），这些框架可以根据测量值浓度来模拟变化的方差。
我们模拟了四种不精密度分布，从简单的恒定方差到恒定方差和比例方差的复杂混合物，并用上述四种回归框架以及不同数量的 CS、数据转换和 RM 相对于 CS 浓度的位置，考察了对可互换性评估结果的影响。回归框架的性能是通过预测区间或百分位数的可互换性从相对 RM 浓度的假拒绝率来确定的，并与预期的名义概率覆盖率进行了比较。
在简单方差分布（恒定或比例方差）中，德明回归，无论是不带对数变换还是带对数变换，都是最有效的方法。在混合方差分布中，具有平滑技术的 GAMLSS 更为合适，需要考虑增加 CS 的数量和 RM 的相对位置。在分析变异系数分布呈 U 形的情况下，即使是更灵活的回归框架也可能不完全适用。
在可互换性评估中，测量程序的方差分布和 RM 相对于临床样本浓度的位置会显著影响可互换性的假拒绝率。
