Precision Analysis

Every precision analysis must begin by setting a precision goal or precision acceptance criterion. The experiment involves repeated measuring of a known amount of a sample analyte. The larger the number of replicates measured during the experiment, the greater the accuracy of the precision estimate. The results of such analysis should be reviewed for the presence of outliers. Since outliers cannot be defined arbitrarily, they should be assessed using acceptable methods such as Tukey’s rule.9

Tukey’s rule proposed that observations lying at least 1.5 times the inter-quartile range (the difference between the first and third quartiles) beyond one of the quartiles could be removed from an analysis. Such observations can be discerned by summarizing data using a boxplot (see Figure 5). However, during the assay development stages, removing such outliers is not advisable, unless a well-founded reason is identified. At the same time, outliers resulting from operator error, contamination, or mechanical failure should be removed.Precision is determined by calculating the mean, the standard deviation with a 95% confidence interval, and the coefficient of variation (CV) of the data. Although the CV has been widely used, it is not useful in all cases. For example, in negative analyte cases in which the signal approaches zero, the mathematical result will be an infinitely large CV that does not correctly reflect the assay’s precision. Presenting the standard deviation and its corresponding 95% confidence interval is preferred. The pass-fail criteria for this type of precision analysis involve assessing whether or not the standard deviation of the estimate exceeds the precision goal.

In addition, other more-complex parameters for within-run precision and total-run precision are required for assay validation. The experimental process required to obtain data for such parameters is defined in the CLSI guidelines in document EP5.7 To assess data outliers using this method, guidelines necessitating a preliminary run should be followed. Once the data are collected for the course of the experiment, it should be assessed using variance component analysis and analysis of variance to determine within-run and total-run precision, as well as the 95% confidence intervals. The results are then scrutinized against the predetermined precision acceptance criteria.

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