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Quantalization of continuous data for benchmark dose estimation

D W Gaylor1

  • 1National Center for Toxicological Research, U.S. Food and Drug Administration, Jefferson, Arkansas, 72079, USA.

Regulatory Toxicology and Pharmacology : RTP
|December 1, 1996
PubMed
Summary
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Converting continuous toxicological data to quantal data for benchmark dose estimation is not recommended. Continuous data analysis provides more precise benchmark dose estimates with narrower confidence intervals compared to quantalized data.

Area of Science:

  • Toxicology
  • Risk Assessment
  • Biostatistics

Background:

  • Benchmark doses (BMDs) are increasingly used to replace the no-observed-adverse-effect level (NOAEL) for regulatory risk assessments.
  • Establishing allowable daily intakes and reference doses relies on accurate BMD estimation.
  • Dose-response data can be continuous (e.g., weight change) or quantal (presence/absence of disease).

Purpose of the Study:

  • To compare the precision of benchmark dose estimation using continuous data versus quantalized (binary) data.
  • To evaluate the impact of data conversion on the accuracy of risk assessment parameters.
  • To determine the optimal data type for reliable BMD calculation under sublinear dose-response curves.

Main Methods:

  • Dose-response data from sublinear curves were analyzed using both continuous and quantalized approaches.

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  • Benchmark doses were estimated for each data type across a range of risk probabilities.
  • Precision was assessed by comparing the lower 95% confidence limits to the true benchmark dose.
  • Sample sizes were varied, with a focus on 5 animals/dose for continuous and 10-20 animals/dose for quantal data.

    • Main Results:

    • Continuous data analysis generally yielded more precise benchmark dose estimates.
    • Confidence intervals for BMDs derived from continuous data were typically within a factor of 2 of the true value.
    • Quantalized data resulted in less precise BMD estimates, with confidence intervals often 3-4 times lower than the true value.
    • Fewer animals per dose (5) were often sufficient for precise BMD estimation with continuous data compared to quantalized data.

    Conclusions:

    • Estimating benchmark doses using continuous toxicological data is more precise than using converted quantalized data.
    • Converting continuous data to quantal data for risk assessment is not recommended due to loss of precision.
    • Continuous data analysis provides more reliable and accurate benchmark dose values for regulatory decision-making.