Standardizing Scoring Methods

Due to plate-to-plate variations from different days or runs a normalizing step is necessary to render the data comparable across entire screens. We have developed several KNIME nodes for popular normalization methods in HTS such as POC, normalized percentage inhibition (NPI), standard score (z-score), and 5-score (26). For all nodes, robust statistics, grouping, negative control, and parameters can be chosen. The method chosen for normalization is dependent on the screening results and the normality of the data. A fiill discussion on this issue is beyond the scope of this chapter and the reader is referred to excellent reviews (27, 28). 

The incidence of HER-2 overexpression varies considerably in different tumors. Table 12.4 shows an average score of HER-2 overexpression in representative human solid tumors commonly evaluated in clinical practice. The specimens in this study consisted of resections of primary tumors. Locally advanced and metastatic lesions of the same tumor types may show significantly different incidences of HER-2 overexpression. Infiltrating ductal carcinoma is one of the few tumor categories for which the incidence of HER-2 overexpression is consistent in most studies. These data were obtained using a single, standardized immunohistochemical method (Herceptin). 

Quantitative continuous data may be evaluated by standard statistical methods. It is inappropriate to use parametric statistical methods on semiquantitative data (i.e., renal injury light miscroscopic assessment scores), although appropriate non-parametric methods (e.g., Duncan s rank-sum procedure) may be used. 

There are no standard sets of scoring methods, so the methods available in each tool differ, however, it is generally accurate to say that current scoring algorithms do not provide a single reliable quantitative measure for the match. Instead, combinations of scores are used as an aid to the further interpretation of data using expert knowledge. 

The variation of sensitivity between different sensors was also checked. Calibration curves with five different sensors were performed. A Relative Standard Deviation of 13, 13 and 42% of calibration slopes (sensitivity) were obtained for Cu, Pb and Cd respectively. These variations should have limited consequence on bias and precision when the standard addition method is used. However, for Cd, variations in the limit of quantification between two electrodes could be expected. Finally, the accuracy of the method was evaluated by the measurement of a SWIFT reference material used during the 2nd SWIFT-WFD Proficiency Testing exercise (Table 4.2.2). The reference value was chosen as the consensus value of the selected data population obtained after excluding the outliers. The performances of the device were estimated according to the Z-score (Z) calculation. Based on this score, results obtained with the SPEs/PalmSens method were consistent with those obtained by all methods for Pb and Cu ( Z < 2) while the result was less satisfactory for Cd (2 < Z < 3). 

The section provides a list and discussion of the standards and methods used for managing HS E issues. Even so there is no real sense of how the group is structured, or of roles and responsibilities, and a no score has been awarded. 

Positive, blank, and interferent samples were prepared using standard MECHEM methods. All samples were marked by sampling personnel in a manner that made it impossible for analysts to determine the composition of the sample during analysis. Nomadics personnel and dog handlers were not given any information on sample identity until analysis of samples was completed and results were submitted for scoring (i.e., the tests were conducted in a blind fashion). 

Figure 15.3 This graph shows the scores for a set of 40 compounds, as calculated using the probabilistic scoring method [31]. The compounds are ordered along the x-axis from the highest to lowest scoring and the score is plotted on the y-axis. Error bars indicate the uncertainty (1 standard deviation) in the score...

If a standard form was used in the qualification phase, a system should exist to rank the respondents that are being considered. Such a system can be as simple as the reviewers making a written recommendation summarizing the strong points of their selections. Some companies have developed forms that allow the final scores to be quantified. Appendix C, Sample Toller Assessment—Qjiantitative Format contains such an example. With either method, the client should consider having two or more reviewers discuss the candidates and agree on the recommended finalists. 

An effective preprocessing method is the use of standard normal variates (SNV). This type of standardization boils down to considering each spectmm x, as a set of q observations and calculating their z-scores ... 

Musumarra et al. [43] identified miconazole and other drugs by principal components analysis of standardized thin-layer chromatographic data in four eluent systems. The eluents, ethylacetate methanol 30% ammonium hydroxide (85 10 15), cyclohexane-toluene-diethylamine (65 25 10), ethylacetate chloroform (50 50), and acetone with the plates dipped in potassium hydroxide solution, provided a two-component model that accounts for 73% of the total variance. The scores plot allowed the restriction of the range of inquiry to a few candidates. This result is of great practical significance in analytical toxicology, especially when account is taken of the cost, the time, the analytical instrumentation and the simplicity of the calculations required by the method. 

Indicator and sample selection are not the only choices a researcher has to make when using MAXCOV. A decision also has to be made about interval size, that is, how finely the input variable will be cut. Sometimes it is possible to use raw scores as intervals that is, each interval corresponds to one unit of raw score (e.g., the first interval includes cases that score one on anhedonia, the second interval includes cases that score two). This is what we used in the depression example. This approach usually works when indicators are fairly short and the sample size is very large, since it would allow for a sufficient number of cases with each raw score. In our opinion, this is the most defensible method of interval selection and should be used whenever possible. However, research data usually do not fit the requirements of this approach (e.g., the sample size is too small). Instead, the investigator can standardize indicators and make cuts at a fixed distance from each other (e.g.,. 25 SD), thereby producing intervals that encompass a few raw scores. 

First, the authors examined the distribution of total PCL-R scores using special probability graph paper (Harding, 1949). This method is a predecessor to mixture modeling it allows for estimation of taxon base rate, means, and standard deviations of latent distributions. The procedure suggested the presence of two latent distributions, with the hitmax at the PCL-R total score of 18. Harding s method is appropriate conceptually and simple computationally, but it became obsolete with the advent of powerful computers. On the other hand, there is no reason to believe that it was grossly inaccurate in this study. 

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