Calculation of Benchmark Doses from Continuous Data
- 1 February 1995
- journal article
- Published by Wiley in Risk Analysis
- Vol. 15 (1) , 79-89
- https://doi.org/10.1111/j.1539-6924.1995.tb00095.x
Abstract
A benchmark dose (BMD) is the dose of a substance that corresponds to a prescribed increase in the response (called the benchmark response or BMR) of a health effect. A statistical lower bound on the benchmark dose (BMDL) has been proposed as a replacement for the no‐observed‐adverse‐effect‐level (NOAEL) in setting acceptable human exposure levels. A method is developed in this paper for calculating BMDs and BMDLs from continuous data in a manner that is consistent with those calculated from quantal data. The method involves defining an abnormal response, either directly by specifying a cutoff x0 that separates continuous responses into normal and abnormal categories, or indirectly by specifying the proportion P0 of abnormal responses expected among unexposed subjects. The method does not involve actually dichotomizing individual continuous responses into quantal responses, and in certain cases can be applied to continuous data in summarized form (e.g., means and standard deviations of continuous responses among subjects in discrete dose groups). In addition to specifying the BMR and either x0 or P0, the method requires specification of the distribution of continuous responses, including specification of the dose‐response θ(d) for a measure of central tendency. A method is illustrated for selecting θ(d) to make the probability of an abnormal response any desired dose‐response function. This enables the same dose‐response model (Weibull, log‐logistic, etc.) to be used for the probability of an abnormal response, regardless of whether the underlying data are continuous or quantal. Whenever the continuous responses are normally distributed with standard deviation σ (independent of dose), the method is equivalent to defining the BMD as the dose corresponding to a prescribed change in the mean response relative to σ.Keywords
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