Analyzing bioassay data using Bayesian methods -- A primer

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The classical statistics approach used in health physics for the interpretation of measurements is deficient in that it does not allow for the consideration of needle in a haystack effects, where events that are rare in a population are being detected. In fact, this is often the case in health physics measurements, and the false positive fraction is often very large using the prescriptions of classical statistics. Bayesian statistics provides an objective methodology to ensure acceptably small false positive fractions. The authors present the basic methodology and a heuristic discussion. Examples are given using numerically generated and real bioassay data ... continued below

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24 p.

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Miller, G.; Inkret, W.C. & Schillaci, M.E. October 16, 1997.

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Description

The classical statistics approach used in health physics for the interpretation of measurements is deficient in that it does not allow for the consideration of needle in a haystack effects, where events that are rare in a population are being detected. In fact, this is often the case in health physics measurements, and the false positive fraction is often very large using the prescriptions of classical statistics. Bayesian statistics provides an objective methodology to ensure acceptably small false positive fractions. The authors present the basic methodology and a heuristic discussion. Examples are given using numerically generated and real bioassay data (Tritium). Various analytical models are used to fit the prior probability distribution, in order to test the sensitivity to choice of model. Parametric studies show that the normalized Bayesian decision level k{sub {alpha}}-L{sub c}/{sigma}{sub 0}, where {sigma}{sub 0} is the measurement uncertainty for zero true amount, is usually in the range from 3 to 5 depending on the true positive rate. Four times {sigma}{sub 0} rather than approximately two times {sigma}{sub 0}, as in classical statistics, would often seem a better choice for the decision level.

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24 p.

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INIS; OSTI as DE99000674

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  • Workshop on Bayesian statistical methods for bioassay, radiochemistry, and internal dosimetry, Charleston, SC (United States), 13-14 Nov 1997

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  • Other: DE99000674
  • Report No.: LA-UR--98-1927
  • Report No.: CONF-9711207--
  • Grant Number: W-7405-ENG-36
  • DOI: 10.2172/676932 | External Link
  • Office of Scientific & Technical Information Report Number: 676932
  • Archival Resource Key: ark:/67531/metadc704467

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  • October 16, 1997

Added to The UNT Digital Library

  • Sept. 12, 2015, 6:31 a.m.

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  • May 5, 2016, 6:18 p.m.

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Miller, G.; Inkret, W.C. & Schillaci, M.E. Analyzing bioassay data using Bayesian methods -- A primer, report, October 16, 1997; New Mexico. (digital.library.unt.edu/ark:/67531/metadc704467/: accessed June 20, 2018), University of North Texas Libraries, Digital Library, digital.library.unt.edu; crediting UNT Libraries Government Documents Department.