Dataset Information

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

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.

SUBMITTER: Pena EA

PROVIDER: S-EPMC5173468 | biostudies-literature | 2017 Apr

REPOSITORIES: biostudies-literature

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

Peña Edsel A EA Wu Wensong W Piegorsch Walter W West Ronald W RW An LingLing L

Risk analysis : an official publication of the Society for Risk Analysis 20160620 4

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-dippin ...[more]

PMID: 27322778

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Dataset Information

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

Publications

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

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