Engineering Cluster, Singapore Institute of Technology, Singapore.
Flinders University International Centre for Point-of-Care Testing, Flinders Health and Medical Research Institute, Flinders University, Adelaide, Australia.
Clin Biochem. 2023 Apr;114:86-94. doi: 10.1016/j.clinbiochem.2023.02.007. Epub 2023 Feb 21.
This simulation study was undertaken to assess the statistical performance of six commonly used rejection criteria for bias detection.
The false rejection rate (i.e. rejection in the absence of simulated bias) and the probability of bias detection were assessed for the following: difference in measurements for individual sample pair, the mean of the paired differences, t-statistics (paired t-test), slope < 0.9 or > 1.1, intercept > 50% of the lower limit of measurement range, and coefficient of determination (R) > 0.95. The linear regressions evaluated were ordinary least squares, weighted least squares and Passing-Bablok regressions. A bias detection rate of < 50% and false rejection rates of >10% are considered unacceptable for the purpose of this study.
Rejection criteria based on regression slope, intercept and paired difference (10%) for individual samples have high false rejection rates and/ or low probability of bias detection. T-statistics (α = 0.05) performed best in low range ratio (lowest-to-highest concentration in measurement range) and low imprecision scenarios. Mean difference (10%) performed better in all other range ratio and imprecision scenarios. Combining mean difference and paired-t test improves the power of bias detection but carries higher false rejection rates.
This study provided objective evidence on commonly used rejection criteria to guide laboratory on the experimental design and statistical assessment for bias detection during method evaluation or reagent lot verification.
本模拟研究旨在评估六种常用的偏倚检测拒绝标准的统计性能。
评估以下六种常用的偏倚检测拒绝标准的错误拒绝率（即在不存在模拟偏倚的情况下的拒绝）和偏倚检测概率：个体样本对的测量值差异、配对差值的平均值、t 统计量（配对 t 检验）、斜率<0.9 或>1.1、截距>测量范围下限的 50%、决定系数（R）>0.95。评估的线性回归包括普通最小二乘法、加权最小二乘法和 Passing-Bablok 回归。对于本研究，拒绝标准的偏倚检测率<50%和错误拒绝率>10%是不可接受的。
基于回归斜率、截距和配对差值（10%）的个体样本的拒绝标准具有较高的错误拒绝率和/或较低的偏倚检测概率。在低范围比（测量范围内最低到最高浓度）和低精密度情况下，t 统计量（α=0.05）表现最佳。在所有其他范围比和精密度情况下，平均差值（10%）表现更好。结合平均差值和配对 t 检验可提高偏倚检测的功效，但会导致更高的错误拒绝率。
本研究为常用的拒绝标准提供了客观的证据，以指导实验室在方法评估或试剂批验证期间进行偏倚检测的实验设计和统计评估。
