96 Shirley Rd, Roseville, NSW, Australia.
Discipline of Laboratory Medicine, School of Health and Biomedical Sciences, RMIT University, Bundoora, VIC, Australia.
Adv Clin Chem. 2018;85:149-211. doi: 10.1016/bs.acc.2018.02.003. Epub 2018 Apr 5.
In Part II of this review we consider the very common case of multiple inputs to a measurement process. We derive, using only elementary steps and the basic mathematics covered in Part I, the formula for the propagation of uncertainties from the inputs to the output. The Gaussian density distribution is briefly explained, since an understanding of this distribution is needed for the determination of so-called expanded uncertainties at the end of a measurement process. The propagation formula in general involves correlations among the inputs, although in many cases these correlations can be considered negligible. Correlations, however, need to be taken into account in related matters such as line-fitting and have particular relevance to method comparisons. These topics are addressed briefly. We next discuss the important question of bias and its incorporation into the expression of uncertainty. We present, finally, six real-world cases in clinical chemistry where uncertainty in the estimated value of the measurand is calculated using the propagation formula.
在本综述的第二部分，我们考虑了测量过程中多个输入的常见情况。我们仅使用第一部分涵盖的基本步骤和基础数学知识，从输入推导出输出的不确定度传播公式。简要解释了高斯密度分布，因为在测量过程结束时确定所谓的扩展不确定度需要了解这种分布。传播公式通常涉及输入之间的相关性，尽管在许多情况下，这些相关性可以被认为是可以忽略不计的。然而，相关性需要在相关问题中考虑，例如线性拟合，并且与方法比较特别相关。这些主题简要讨论。接下来，我们讨论了偏倚的重要问题及其在不确定度表示中的纳入。最后，我们提出了临床化学中六个实际案例，其中使用传播公式计算被测值的估计值的不确定度。
