Validation robustness

OECD also provides a check list for the application of its principles in the context of QSAR validation. This checklist can be useful to help scientists and regulators during the selection of a QSAR model and to evaluate its robustness/ validity [9]. 

As for all statistical approaches, there is a need for robust validation to ensure that the resulting models are not misleading due to chance correlation and that these models show predictive power to novel molecules. Hence, the successful application depends on the chosen validation strategy using internal validation and evaluation using an external test set. Furthermore, the choice of a set of appropriate descriptors leading to directly interpretable 3D-QSAR models is crucial for interpretation and discussions with medicinal chemists. 

To illustrate the power of validated QSAR models as virtual sereening tools we diseuss the examples of studies that resulted in experimentally eonfirmed hits. Sueh studies eould only be performed if there is sufficient data available for a series of tested compounds such that robust validated models could be developing using the workflow described in Figure 10.1. 

Although CV may seem a robust validation technique, some difficulties should not be overlooked. Variables that do not contribute to prediction, that is, cause noise in the... 

DOE is the fastest route to a profitable, reliable, robust, validated process. DOE s requirement of a rigorous design methodology that passes peer review with the scientists most knowledgeable about the process ensures scientific soundness. The depth of DOE s statistical foundation that enables the measurement of multiple effects and interactions in a single set of experiments proves DOE s statistical validity. DOE is also a resource conservator, since it requires less time and... 

To conclude, it can be said that speciation techniques are being improved due to the growing importance of speciation in the monitoring of trace metals. There is considerable pressure for speciation methods to be used in routine analysis, particularly with regard to legislative demands. However, this is only likely to be achieved with the development of robust validated methods utilising relevant soil reference materials in order to obtain reliable data. 

One of our interests is to develop such miniaturized instruments for use in clinical diagnosis, in a point-of-care setting. In many respects, instrument development is a far less challenging task than the development of robust, validated biomarkers along with the means to present these to the instrument in some selective fashion. This is likely to involve some form of affinity or immunoprecipitation step that can be integrated directly with sample introduction system. 

Note PRESS and q may relate to any property that is being modelled and not just activity , will always be smaller than r. When q > 0.3, a model is considered significant. Although cross-validation may seem a robust validation technique, some difficulties should not be overlooked. Variables that do not contribute to prediction, i.e. cause noise in the model, may have detrimental effects on CV. This may particularly play a role when many variables have to be considered, such as in a 3D-QSAR CoMFA analysis (see Chapter 25). A procedure for variable selection in the case of many variables has been developed and is named GOLPE (generating optimal linear PLS estimations). ... 

Robust validation data for residual-stress models require experimentally intensive and costly diffraction testing, using neutrons or x-rays. The particular value of synchrotron x-ray techniques has been illustrated for several aluminum welding studies, including FSW applied to dissimilar alloys (Ref 88-90). Bringing together the finite element analysis of residual stress and the extensive synchrotron data is a matter of current research. 

The WEKA software suite [23] has been used in carrying out the experiments. The results were evaluated using Accuracy (Acc). For the training and validation steps, we used k-fold cross-validation with k = 10. Cross-validation is a robust validation method for variable selection [24]. Repeated cross-validation (as calculated by the WEKA environment) allows robust statistical tests. We also use the measurement provided automatically by WEKA Coverage of cases (0.95 level). 

As with the seven qualified DIKI biomarkers, there will be the need for the implementation of a robust validation strategy which will be based on the... 

Examples of robustness validation of spectroscopic methods, for example, NIR systems, are also found in the literature and include evaluation of the partial least squares (PLS) algorithms used in the regression analysis, as well as more conventional parameters such as slit width, wavelength accuracy, and time constants. 

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