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

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

Research output: Contribution to journalArticle

2 Scopus citations


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)
JournalRisk Analysis
StateAccepted/In press - 2016


  • 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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