Download PDF by Julia Pet-Edwards, Yacov Y. Haimes, Vira Chankong, Herbert: Risk Assessment and Decision Making Using Test Results: The

By Julia Pet-Edwards, Yacov Y. Haimes, Vira Chankong, Herbert S. Rosenkranz, Fanny K. Ennever (auth.)

ISBN-10: 1468455958

ISBN-13: 9781468455953

ISBN-10: 1468455974

ISBN-13: 9781468455977

The problems linked to making hazard tests at the foundation of experimental effects are normal to practitioners in lots of fields­ engineering, epidemiology, chemistry, and so on. those problems are rather universal in difficulties that experience dynamic and stochastic features pushed by means of a number of reasons and targets, with advanced interconnections and inter­ dependencies. buying a suitable information base, processing and interpreting version effects, and transmitting those effects at a suitable technical, social, political, and institutional point are extra problems that needs to be addressed. This ebook is grounded at the premise that dangers are most sensible assessed at the foundation of experimental effects and sound mathematical analyses, coupled with the information of specialists. The carcinogenicity prediction and battery choice (ePBS) technique defined herein presents a scientific mechan­ ism-a synthesis of platforms and statistical and selection analyses-to relief researchers and choice makers within the severe box of carcinogenicity prediction in opting for a suitable battery of assessments to exploit and in translating experimental effects into info that may be used as an relief to determination making.

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1. I--- validation A priori information Methodological approach used in exploratory data analysis. After completing the cluster analysis methodology, the next step is to explore the results and to try to explain or justify why the particular data items formed groups. In this step, information other than that found in the data base is considered. This additional information is used in formulating a hypothesis that is consistent with the cluster analysis results. For example, suppose that the measurements are clinical tests on a set of patients and that we are interested in seeing which of the patients form groups based on the results of the tests.

VerificationNalidation of Clusters The formation of clusters by any of the three previously described methods is strongly related to the choice of similarity or proximity measure used. For our previous example, the similarity between two tests was measured as the percentage (or ratio) of matching test results. Thus, it is a measure of the similarity between the responses of the tests rather than a direct measurement of the tests themselves. , is not used in the formation of the clusters. Verification and validation of clusters involves closely examining the clusters and trying to determine whether the results characterize the data.

The best-compromise solution (policy) is more likely to be reached in the neighborhood of B or C than in the neighborhood of A or D. As a second illustration, consider the situation in which a chemical company has just completed a battery of tests on a chemical with unknown carcinogenic potential. Suppose that the current results were inconclusive, and that the choice must be made as to whether additional testing should be used, and if so what tests should be used. We could formulate the problem (model the problem) in terms of minimizing the cost II (x) of the additional test(s) and minimizing the rate of getting false positives I2(x) (which can be given by one minus the specificity) subject to x E X, where X denotes the set of all feasible batteries of tests.

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Risk Assessment and Decision Making Using Test Results: The Carcinogenicity Prediction and Battery Selection Approach by Julia Pet-Edwards, Yacov Y. Haimes, Vira Chankong, Herbert S. Rosenkranz, Fanny K. Ennever (auth.)

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