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

TL;DR

This paper compares various statistical methods for estimating benchmark doses from quantal-response data, emphasizing model selection, averaging, and nonparametric approaches, with simulation and real data illustrations.

Contribution

It introduces and evaluates multiple approaches for BMD estimation considering model uncertainty and data-driven challenges in risk assessment.

Findings

Model-averaging improves BMD estimation accuracy.

Simulation shows trade-offs among different model selectors.

Real data example demonstrates practical applicability.

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.