Model Selection and Estimation with Quantal-Response Data in Benchmark Risk Assessment

Edsel A. Peña, Wensong Wu, Walter Piegorsch, Ronald W. West, Ling Ling An

Research output: Contribution to journalArticlepeer-review

2 Scopus citations

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 languageEnglish (US)
Pages (from-to)716-732
Number of pages17
JournalRisk Analysis
Volume37
Issue number4
DOIs
StatePublished - 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)

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