Estimating Assay Performance Characteristics

The limits of quantification of an analytical procedure are the lowest and largest amounts of the targeted substance in the sample that can be quantitatively determined under the prescribed experimental conditions with well-established measurement error (analytical bias and precision). Consequently, the dynamic range of an analytical procedure is the range between the lower and the upper limits of quantification within which a measured result is expected to have acceptable levels of bias and precision. 

The limit of detection of an assay is the lowest amount of the targeted substance in the sample that can be detected almost surely, but not necessarily quantified with acceptable level of bias and precision using the experimental conditions prescribed. A popular method for estimating the LOD that has been widely used in the literature is to interpolate the concentration corresponding to mean plus 3SD of the assay signal of a background sample. From experience, LOD tends to be three times less than LLOQ. 

Performance characteristics of ligand-binding assays are estimated using calculated concentrations from the spiked validation samples during prestudy validation and/or from the quality control samples during in-study validation. 

The primary performance measures of a ligand-binding assay are bias/trueness and precision. These measures along with the total error are then used to derive and evaluate several other performance characteristics such as sensitivity (LLOQ), dynamic range, and dilutional linearity. Estimation of the primary performance measures (bias, precision, and total error) requires relevant data to be generated from a number of independent runs (also termed as experiments or assay s). Within each run, a number of concentration levels of the analyte of interest are tested with two or more replicates at each level. The primary performance measures are estimated independently at each level of the analyte concentration. This is carried out within the framework of the analysis of variance (ANOVA) model with the experimental runs included as a random effect [23]. Additional terms such as analyst, instmment, etc., may be included in this model depending on the design of the experiment. This ANOVA model allows us to estimate the overall mean of the calculated concentrations and the relevant variance components such as the within-run variance and the between-run variance. 

The trueness of the method can be represented by the percent relative error (bias). At concentration level j, it is given by 

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Precision and Accuracy. Precision and accuracy are assay performance characteristics that describe the random (statistical) errors and systematic errors (bias) associated with repeated measurements of the same sample under specified conditions [3-5]. Precision is typically estimated by the percent coefficient of variation (% CV, also referred to as relative standard deviation or RSD) but may certainly also be reported as standard deviations. Method accuracy is expressed as the percent relative error (% RE) and is determined by the percent deviation of the weighted samples mean from samples with nominal reference values. A collection of validation sample statistics can be found in References 9,11, and 25. 

Hapten density is important for both immunization and assay performance, and hence the extent of conjugation or hapten density should be confirmed by established methods. A characteristic ultraviolet (UV) or visible absorbance spectrum that distinguishes the hapten from the carrier protein or use of a radiolabeled hapten can be used to determine the degree of conjugation. If the hapten has a similar A. iax to the protein, the extent of incorporation can still be estimated when the concentration of the protein and the spectral characteristics of the hapten and protein are known. The difference in absorbance between the conjugate and the starting protein is proportional to... 

In the code, oper reads in the level of operator, day reads in the level of day, assay reads in the assay, acc is Y for accuracy and is defined by the ratio of observed mass to expected mass ( exp ). Note that the BY exp the procedure is run separately for each level of the expected mass using exp as the variable name. In this manner, the precision of the accuracy and the variance components are estimated independently at each level of the expected mass. Thus, we obtain picture of the assay s performance characteristics across the operational range. As assay is nested in the interaction between operator and day, the same analysis can be coded used oper day in place of assay in the previuos code. The CL and cl in the procedure line and model statement are... 

When feasibility studies indicate that an assay has potential for field application, the next step is to characterize the assay s performance characteristics. Estimates are needed of diagnostic sensitivity (D-SN) and diagnostic specificity (D-SP). 

When the new test is evaluated by comparison with another serological test or combination of tests, the estimates of D-SN and D-SP for the new test are called relative diagnostic sensitivity and relative diagnostic specificity. These standards of comparison, however, have their own established levels of false positivity and false negativity that are sources of error carried over into the new assay. Therefore, the relative D-SN and D-SP for the new test will be underestimated. It follows that the greater the amount of false positivity and false negativity in the test that is used as the standard of comparison, the more the new assay s performance characteristics will be undermined. In other... 

Validation of assays can be made in the absence of a standard. The validation then relies on statistical tools such as cluster or mixture analysis. Assuming that a few sera of known status are available to establish the feasibility of the assay system, it is possible to obtain a rough estimate of the assay s performance characteristics. Then, several thousands of animals in the target population can be tested in the absence of known infection status data other than possibly scattered clinical observations. If a clear bimodal frequency distribution becomes evident with a large peak consisting of many animals at the low... 

The purpose of method validation is to demonstrate that an analytical method is suitable for its intended purpose and, for a quantative method, provides a reasonable estimate of the true value of the sample tested. Appropriate performance characteristics, such as accuracy and precision, must be demonstrated before making decisions based on test data. Method validation involves assessing method performance against predefined criteria, established based on the sample specifications and the type of measurement to be performed, for example, assay, identification, or limit test. A rigorous assessment of method performance versus predefined criteria provides assurance that the method will consistently provide a fit for purpose performance. Method characteristics to be evaluated during method validation are described by several guidelines [1,2] some of which are shown in Tables 3.1 and 3.2. 

Five transformants produced a major secreted protein band, with an estimated molecular weight of 40 kDa and no protein was detected in the control. The estimated molecular weight is greater than that predicted or observed from E. coli, which is likely due to post-translational modification. P6 and P10 transformants were retained to perform enzymatic assays. The culture supernatants were assays for activity against methyl caffeate (MCA) and methyl ferulate (MFA). The recombinant proteins were shown to be active as a feruloyl esterase and show the characteristics of a type B ferulic acid esterase.6 Feruloyl esterase activity is reported in Table 1. 

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