Titlebook: Risk Assessment and Decision Making Using Test Results; The Carcinogenicity Julia Pet-Edwards,Yacov Y. Haimes,Fanny K. Ennever Book 1989 P

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Risk Assessment Using Test Results the unknown property might be the potential carcinogenicity of the chemical. Suppose, further, that we have a set of tests that we can use to help us determine whether the property is present in the object. In Chapter 3 we discussed several analyses for computing the performances of the tests and t

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Preliminary Analysisg the property of interest, as well as the interrelationships among the tests. In this case, the analysis of data on the tests (i. e., the preliminary analysis step) may be omitted in its entirety or portions may be skipped.

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Applications of CPBS to Cancer Hazard IdentificationEnnever, 1988a). Also, it appears that we have no choice but to continue using short-term tests since the other alternatives are (a) not to test but to wait for untoward effects in our exposed human population and (b) to continue relying solely on animal bioassays.

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EpilogueBayesian theory and provides a systematic mechanism — a synthesis of systems, statistical, and decision analyses — for transferring information from tests and experts into results which can aid decision making.

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Fundamental Basics of the CPBS Approachion of test results can be problematic because there often exists a variable amount of information overlap (positive dependence) and differences (negative dependence) among the tests. It is a difficult problem to account for both the imperfection of the individual tests as well as their interdependencies in their joint interpretation.

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Risk Assessment Using Test Resultshe interdependencies between the pairs of tests, and in Chapter 4 we discussed how one can select the “best” battery of tests to use for a particular application. In this chapter, it is assumed that we have chosen a battery to use, we have applied this battery on the object, and now we must interpret the results of this battery.

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Book 1989g, epidemiology, chemistry, etc. These difficulties are particularly common in problems that have dynamic and stochastic characteristics driven by multiple purposes and goals, with complex interconnections and inter- dependencies. Acquiring an appropriate data base, processing and analyzing model re

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