Literature DB >> 11262759

The importance of the prediction model in the validation of alternative tests.

A P Worth1, M Balls.   

Abstract

An overview is presented of the validation process adopted by the European Centre for the Validation of Alternative Methods, with particular emphasis on the central role of the prediction model (PM). The development of an adequate PM is considered to be just as important as the development of an adequate test system, since the validity of an alternative test can only be established when both components (the test system and the PM) have successfully undergone validation. It is argued, however, that alternative tests and their associated PMs do not necessarily need to undergo validation at the same time, and that retrospective validation may be appropriate when a test system is found to be reliable, but the case for its relevance remains to be demonstrated. For an alternative test to be considered "scientifically valid", it is necessary for three conditions to be fulfilled, referred to here as the criteria for scientific relevance, predictive relevance, and reliability. A minimal set of criteria for the acceptance of any PM is defined, but it should be noted that required levels of predictive ability need to be established on a case-by-case basis, taking into account the inherent variability of the alternative and in vivo test data. Finally, in view of the growing shift in emphasis from the use of stand-alone alternative tests to alternative testing strategies, the importance of making the PM an integral part of the testing strategy is discussed.

Mesh:

Year:  2001        PMID: 11262759     DOI: 10.1177/026119290102900210

Source DB:  PubMed          Journal:  Altern Lab Anim        ISSN: 0261-1929            Impact factor:   1.303


  2 in total

1.  Mechanistic validation.

Authors:  Thomas Hartung; Sebastian Hoffmann; Martin Stephens
Journal:  ALTEX       Date:  2013       Impact factor: 6.043

Review 2.  Methods for reliability and uncertainty assessment and for applicability evaluations of classification- and regression-based QSARs.

Authors:  Lennart Eriksson; Joanna Jaworska; Andrew P Worth; Mark T D Cronin; Robert M McDowell; Paola Gramatica
Journal:  Environ Health Perspect       Date:  2003-08       Impact factor: 9.031

  2 in total

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