Analyzing bioassay data using Bayesian methods -- A primer

Journal Article · Thu Jun 01 04:00:00 EDT 2000 · Health Physics

DOI:https://doi.org/10.1097/00004032-200006000-00002· OSTI ID:20075789

Miller, G; Inkret, W C; Schillaci, M E; Martz, H F; Little, T T

The classical statistics approach used in health physics for the interpretation of measurements is deficient in that it does not take into account needle in a haystack effects, that is, correct identification of events that are rare in a population. This is often the case in health physics measurements, and the false positive fraction (the fraction of results measuring positive that are actually zero) is often very large using the prescriptions of classical statistics. Bayesian statistics provides a methodology to minimize the number of incorrect decisions (wrong calls): false positives and false negatives. The authors present the basic method and a heuristic discussion. Examples are given using numerically generated and real bioassay data for 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 for typical situations involving rare events 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 in the range of 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 seem a better choice for the decision level in these situations.

• Research Organization: Los Alamos National Lab., NM (US)
• OSTI ID: 20075789
• Journal Information: Health Physics, Journal Name: Health Physics Journal Issue: 6 Vol. 78; ISSN HLTPAO; ISSN 0017-9078
• Country of Publication: United States
• Language: English

Related Subjects

61 RADIATION PROTECTION AND DOSIMETRY
BIOASSAY
DATA COVARIANCES
DOSIMETRY
RADIATION DOSES
STATISTICS
TRITIUM