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Hybrid method benchmark dose estimation with a heterogeneous variance structure.

Jens Riis Baalkilde1, Signe Marie Jensen1

  • 1Department of Plant and Environmental Science, University of Copenhagen, Taastrup, Denmark.

Integrated Environmental Assessment and Management
|November 14, 2025
PubMed
Summary
This summary is machine-generated.

The benchmark dose (BMD) methodology, used for toxic substance safety, is improved by a new hybrid method. This method addresses variance heterogeneity, reducing bias and improving estimates for safe exposure levels.

Keywords:
benchmark dosedose-response analysisheteroscedasticityhybrid methodmodel averaging

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Area of Science:

  • Toxicology
  • Biostatistics
  • Risk Assessment

Background:

  • The benchmark dose (BMD) methodology is standard for estimating safe exposure levels.
  • The hybrid method, commonly used for continuous response variables, assumes constant standard deviation.
  • Violations of this assumption can lead to biased BMD and BMDL estimates.

Purpose of the Study:

  • To introduce an extended class of dose-response models accommodating varying standard deviations.
  • To adapt the hybrid method for situations with heteroscedasticity.
  • To improve the accuracy and reliability of BMDL estimation in toxicology.

Main Methods:

  • Developed extended dose-response models allowing for variance heterogeneity.
  • Adapted the hybrid method to incorporate these new models.
  • Utilized simulation studies and real-world datasets with heteroscedasticity.

Main Results:

  • The proposed method effectively handles variance heterogeneity in dose-response data.
  • Addressing variance heterogeneity significantly reduces bias in BMDL estimates.
  • The adapted hybrid method yields BMDL estimates with improved coverage closer to the nominal level.

Conclusions:

  • The adapted hybrid method provides more accurate and reliable BMDL estimates when standard deviation varies.
  • This advancement enhances the safety assessment of toxic substances.
  • Accounting for heteroscedasticity is crucial for robust risk assessment.