Example validation data analysis

Light emission from the chemiluminescent substrate is directly proportional to the amount of the target nucleic acid in the sample, and the results are recorded as relative luminescence units (RLUs). All samples, standards, and controls are run in duplicate, and the mean RLU is used in data analysis. The percent coefficient of variation (%CV) for duplicate RLU for controls and samples must be within the recommended limit for that assay for the results to be valid. For example, negative samples must have a CV of <30% and positive samples <20% in the HCV assay. 

Traditionally, data was a single numerical result from a procedure or assay for example, the concentration of the active component in a tablet. However, with modem analytical equipment, these results are more often a spectrum, such as a mid-infrared spectrum for example, and so the use of multivariate calibration models has flourished. This has led to more complex statistical treatments because the result from a calibration needs to be validated rather than just a single value recorded. The quality of calibration models needs to be tested, as does the robustness, all adding to the complexity of the data analysis. In the same way that the spectroscopist relies on the spectra obtained from an instrument, the analyst must rely on the results obtained from the calibration model (which may be based on spectral data) therefore, the rigor of testing must be at the same high standard as that of the instrument... 

Principal component analysis is central to many of the more popular multivariate data analysis methods in chemistry. For example, a classification method based on principal component analysis called SIMCA [69, 70] is by the far the most popular method for describing the class structure of a data set. In SIMCA (soft independent modeling by class analogy), a separate principal component analysis is performed on each class in the data set, and a sufficient number of principal components are retained to account for most of the variation within each class. The number of principal components retained for each class is usually determined directly from the data by a method called cross validation [71] and is often different for each class model. 

There are many approaches used for PPK model development in the literature. These range from modeling population pharmacokinetic data without exploratory data analysis to approaches that incorporate the latter. Excellent examples of population pharmacokinetic model development, which incorporate exploratory data analysis into population pharmacokinetic model development, can be found in the articles by Ette and Ludden and Mandema, Verotta, and Sheiner Excellent reviews on the validation of PPK models are available in the literature. Thus, validation will not be discussed. 

Abstract Validation of analytical methods of well-characterised systems, such as are found in the pharmaceutical industry, is based on a series of experimental procedures to establish selectivity, sensitivity, repeatability, reproducibility, linearity of calibration, detection limit and limit of determination, and robustness. It is argued that these headings become more difficult to apply as the complexity of the analysis increases. Analysis of environmental samples is given as an example. Modern methods of analysis that use arrays of sensors challenge validation. The output may be a classification rather than a concentration of analyte, it may have been established by imprecise methods such as the responses of human taste panels, and the state space of possible responses is too large to cover in any experimental-design procedure. Moreover the process of data analysis may be done by non-linear methods such as neural networks. Validation of systems that rely on computer software is well established. 

The contention of this paper is that apart from this example, methods used for other purposes are often not well validated, and as increasing use is made of multi-sensor methods involving intelligent data analysis, there may not be even a prospect of validation in the traditional sense. 

Data exploration is a scientific exercise where we try to learn things about the data, for example, how the covariates are distributed and how they relate to each other. The exploratory data analysis also defines the population—and thereby the bounds for the validity of the model—and will form the basis for reporting the analysis to others. The data exploration is also important from an error finding point of view since some errors only become apparent when closely studying the data. 

The fifth section is a continuation of the example and contains a hypothetical data set with an analysis. The sixth section discusses statistical analyses and illustrates an analysis of the example validation data. 

Table 12 Summary of Lack-of-Fit Analysis for Linearity of Example Validation Data...

---