Abstract
This article describes several approaches for estimating the benchmark dose (BMD) in a risk assessment study with quantal dose-response data and when there are competing model classes for the dose-response function. Strategies involving a two-step approach, a model-averaging approach, a focused-inference approach, and a nonparametric approach based on a PAVA-based estimator of the dose-response function are described and compared. Attention is raised to the perils involved in data “double-dipping” and the need to adjust for the model-selection stage in the estimation procedure. Simulation results are presented comparing the performance of five model selectors and eight BMD estimators. An illustration using a real quantal-response data set from a carcinogenecity study is provided.
Original language | English (US) |
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Pages (from-to) | 716-732 |
Number of pages | 17 |
Journal | Risk Analysis |
Volume | 37 |
Issue number | 4 |
DOIs | |
State | Published - Apr 2017 |
Keywords
- Focused-inference approach
- information measures
- model averaging
- model selection problem
- pooled adjacent violators algorithm (PAVA)
- quantal-dose response
- two-step estimation approach
ASJC Scopus subject areas
- Safety, Risk, Reliability and Quality
- Physiology (medical)