Mehdi Razzaghi
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ABSTRACT: To establish allowable daily intakes for humans from animal bioassay experiments, benchmark doses corresponding to low levels
of risk have been proposed to replace the no-observed-adverse-effect level for non-cancer endpoints. When the experimental
outcomes are quantal, each animal can be classified with or without the disease. The proportion of affected animals is observed
as a function of dose and calculation of the benchmark dose is relatively simple. For quantitative responses, on the other
hand, one method is to convert the continuous data to quantal data and proceed with benchmark dose estimation. Another method
which has found more popularity (Crump, Risk Anal 15:79–89; 1995) is to fit an appropriate dose–response model to the continuous
data, and directly estimate the risk and benchmark doses. The normal distribution has often been used in the past as a dose–response
model. However, for non-symmetric data, the normal distribution can lead to erroneous results. Here, we propose the use of
the class of beta-normal distribution and demonstrate its application in risk assessment for quantitative responses. The most
important feature of this class of distributions is its generality, encompassing a wide range of distributional shapes including
the normal distribution as a special case. The properties of the model are briefly discussed and risk estimates are derived
based on the asymptotic properties of the maximum likelihood estimates. An example is used for illustration.
Environmental and Ecological Statistics 04/2012; 16(1):25-36. · 1.31 Impact Factor
