title: Quantalization of continuous data for benchmark dose estimation.
abstract: Benchmark doses corresponding to low levels of noncancer disease risk have been proposed to replace the no-observed-adverse-effect level for establishing allowable daily intakes or reference doses. For quantal data each animal is classified with or without a disease. The proportion of animals with an adverse effect (risk) is observed as a function of dose of a toxic substance. The calculation of a benchmark dose is relatively straightforward. For continuous data a somewhat more complicated designation of risk is required. Because of the more direct procedures with quantal data, consideration could be given to converting continuous data to quantal data before estimating benchmark doses. The purpose of this paper is to compare the precision of the two approaches (use of continuous or quantalized data) for a number of sublinear dose-response curves ranging from low to high probabilities of risk at the highest dose. In these studies, five animals per dose were generally satisfactory to estimate the benchmark dose for continuous data, whereas the corresponding quantalized data generally do not perform as well even with 10 to 20 animals per dose. For quantalized data, the lower 95% confidence limits on the estimates of the benchmark dose were generally a factor of 3 to 4 below the true benchmark dose, whereas the confidence limits using the continuous data were generally within a factor of 2 of the true benchmark dose. Although the use of quantalized data for the estimation of risk is more direct, estimates of benchmark doses using the continuous data were more precise. Based on this study, converting continuous data to quantal data is not recommended.
authors.affiliation: National Center for Toxicological Research, U.S. Food and Drug Administration, Jefferson, Arkansas, 72079, USA.
authors.forename: D W
authors.initials: DW
authors.lastname: Gaylor
mesh_terms: D000818:Animals; D003627:Data Interpretation, Statistical; D004305:Dose-Response Relationship, Drug; D008432:Mathematical Computing; D019055:No-Observed-Adverse-Effect Level; D015203:Reproducibility of Results; D018570:Risk Assessment; D018675:Toxicity Tests / Q000379:methods* / Q000706:statistics & numerical data