Model Selection and Estimation with Quantal-Response Data in Benchmark Risk Assessment
Abstract
This paper 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.
Keywords
Cite
@article{arxiv.1411.1015,
title = {Model Selection and Estimation with Quantal-Response Data in Benchmark Risk Assessment},
author = {Edsel A. Pena and Wensong Wu and Walter Piegorsch and Ronald W. West and Lingling An},
journal= {arXiv preprint arXiv:1411.1015},
year = {2019}
}
Comments
44 pages including many figures