Model Selection and Estimation with Quantal-Response Data in Benchmark Risk Assessment.
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.
Department of Statistics, University of South Carolina, Columbia, SC 29208 USA.
Edsel A
EA
Peña
Department of Mathematics and Statistics, Florida International University, Miami, FL 33199 USA.
Wensong
W
Wu
Program in Statistics and BIO5 Institute, University of Arizona, Tucson, AZ 85721 USA.
Walter
W
Piegorsch
Department of Statistics, North Carolina State University, Raleigh, NC 27695 USA.
Ronald W
RW
West
Program of Statistics, Department of Agricultural and Biosystems Engineering, University of Arizona, Tucson, AZ 85721 USA.
LingLing
L
An
D002273:Carcinogens; D003198:Computer Simulation; D004305:Dose-Response Relationship, Drug; D006801:Humans; D015233:Models, Statistical; D019055:No-Observed-Adverse-Effect Level; D012044:Regression Analysis; D018570:Risk Assessment / Q000379:methods*
D002273:Carcinogens
Focused-inference approach; information measures; model averaging; model selection problem; pooled adjacent violators algorithm (PAVA); quantal-dose response; two-step estimation approach