An extended and unified modeling framework for benchmark dose estimation for both continuous and binary data

Details

• Alternative Title:
  Environmetrics
• Personal Author:
  Aerts, Marc ; Wheeler, Matthew W. ; Abrahantes, José Cortiñas
• Description:
  Protection and safety authorities recommend the use of model averaging to determine the benchmark dose approach as a scientifically more advanced method compared with the no-observed-adverse-effect-level approach for obtaining a reference point and deriving health-based guidance values. Model averaging however highly depends on the set of candidate dose-response models and such a set should be rich enough to ensure that a well-fitting model is included. The currently applied set of candidate models for continuous endpoints is typically limited to two models, the exponential and Hill model, and differs completely from the richer set of candidate models currently used for binary endpoints. The objective of this article is to propose a general and wide framework of dose response models, which can be applied both to continuous and binary endpoints and covers the current models for both type of endpoints. In combination with the bootstrap, this framework offers a unified approach to benchmark dose estimation. The methodology is illustrated using two data sets, one with a continuous and another with a binary endpoint.
• Keywords:
  Akaike Information Criterion Benchmark Dose Bootstrap Cumulative Distribution Function Dose Response Maximum Likelihood Model Averaging
• Source:
  Environmetrics. 31(7)
• Pubmed ID:
  36052215
• Pubmed Central ID:
  PMC9432821
• Document Type:
  Journal Article
• Funding:
  CC999999/ImCDC/Intramural CDC HHSUnited States/ ; Z99 ES999999/ImNIH/Intramural NIH HHSUnited States/ ; ZIA ES103358/ImNIH/Intramural NIH HHSUnited States/
• Volume:
  31
• Issue:
  7
• Collection(s):
  CDC Public Access
• Main Document Checksum:
  urn:sha-512:f2dd13481f5b34e37dfecfe8be1bd79eee1139de0e57ab3acc6fe18d391746a83a33dd2ed8f6c954325bc0abf7097022858e6f1c9865c0dbcc672cfe43159bf3
• Download URL:
  https://stacks.cdc.gov/view/cdc/120834/cdc_120834_DS1.pdf
• File Type:
  [PDF - 682.08 KB ]