@article{pena_wu_piegorsch_west_an_2017, title={Model Selection and Estimation with Quantal-Response Data in Benchmark Risk Assessment}, volume={37}, ISSN={["1539-6924"]}, DOI={10.1111/risa.12644}, abstractNote={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.}, number={4}, journal={RISK ANALYSIS}, author={Pena, Edsel A. and Wu, Wensong and Piegorsch, Walter and West, Ronald W. and An, LingLing}, year={2017}, month={Apr}, pages={716–732} }
 @article{piegorsch_an_wickens_west_pena_wu_2013, title={Information-theoretic model-averaged benchmark dose analysis in environmental risk assessment}, volume={24}, ISSN={["1099-095X"]}, DOI={10.1002/env.2201}, abstractNote={An important objective in environmental risk assessment is estimation of minimum exposure levels, called benchmark doses (BMDs), that induce a pre‐specified benchmark response in a dose–response experiment. In such settings, representations of the risk are traditionally based on a specified parametric model. It is a well‐known concern, however, that existing parametric estimation techniques are sensitive to the form used for modeling the dose response. If the chosen parametric model is in fact misspecified, this can lead to inaccurate low‐dose inferences. Indeed, avoiding the impact of model selection was one early motivating issue behind the development of the BMD technology. Here, we apply a frequentist model averaging approach for estimating BMDs, on the basis of information‐theoretic weights. We explore how the strategy can be used to build one‐sided lower confidence limits on the BMD, and we study the confidence limits' small‐sample properties via a simulation study. An example from environmental carcinogenicity testing illustrates the calculations. It is seen that application of this information‐theoretic, model averaging methodology to benchmark analysis can improve environmental health planning and risk regulation when dealing with low‐level exposures to hazardous agents. Copyright © 2013 John Wiley & Sons, Ltd.}, number={3}, journal={ENVIRONMETRICS}, author={Piegorsch, Walter W. and An, Lingling and Wickens, Alissa A. and West, R. Webster and Pena, Edsel A. and Wu, Wensong}, year={2013}, month={May}, pages={143–157} }