Quantifying detrimental risks from exposure to hazardous agents is an important component in the process of risk evaluation and analysis. The focus of this exploratory project is to develop and study new methods of statistical inference for use in quantitative risk assessment. Application is directed to biomedical, occupational, environmental, toxicological, or pharmacological studies where human or animal data are used to set benchmark or other safe, low-dose levels of a hazardous agent, but where study information is limited to high- dose levels of the agent. The resulting guidelines can help improve public health planning and risk regulation when dealing with low-level exposures to hazardous stimuli. Emphasis will be on estimation of the benchmark dose (BMD) associated with a pre-selected level of benchmarked risk (BMR), based on risk/safety data in the form of proportions. Specific focus in this exploratory project centers on ways to avoid the problems of model-dependency and model selection bias, by basing estimation and inferences on model-independent isotonic regression techniques. The theoretical, asymptotic characteristics of the model-free point estimators will be established, and from these large-sample benchmark dose confidence limits (BMDLs) will be constructed. Software developed for BMD estimation and for calculating the confidence limits will be distributed on the Internet, to allow access to the methods by the widest possible corps of users. The methods will fill existing gaps in model-independent methods for benchmark analysis, and will have application to the important problem of modern low-dose risk/safety assessment with proportion data. An evaluation phase of the project will apply the methods to an assortment of risk analytic, quantal response data, assembled from the public domain/existing literature, and will study the small-sample operating characteristics of the methodology via Monte Carlo computer calculations. This will assess the operating capabilities of the new methods and determine if further advances in benchmark analysis can be based on this new estimation paradigm.

Public Health Relevance

This proposal initiates and explores development of model-independent statistical approaches for setting benchmark or other safe low-dose exposure levels of a hazardous stimulus when existing data are limited to high-dose levels of the agent. Common in occupational, environmental, toxicological, and pharmacological risk/safety studies, the methods expand upon traditional benchmark risk analyses and provide insight into two scientifically pressing issues: (i) that most existing benchmark estimation techniques depend heavily on the forms chosen to model the stimulus/dose response;and (ii) that in the face of potential model misspecification/model selection bias, existing statistical strategies for benchmark dose estimation can yield inaccurate, and possibly unsafe, low-dose inferences. This work will explore development of robust benchmark dose inferences, from which resulting low-dose guidelines can improve public health planning and risk regulation when dealing with low-level exposures to hazardous stimuli.

Agency
National Institute of Health (NIH)
Institute
National Institute of Environmental Health Sciences (NIEHS)
Type
Exploratory/Developmental Grants (R21)
Project #
5R21ES016791-02
Application #
7796713
Study Section
Biostatistical Methods and Research Design Study Section (BMRD)
Program Officer
Dilworth, Caroline H
Project Start
2009-04-01
Project End
2012-03-31
Budget Start
2010-04-01
Budget End
2012-03-31
Support Year
2
Fiscal Year
2010
Total Cost
$182,903
Indirect Cost
Name
University of Arizona
Department
Type
Organized Research Units
DUNS #
806345617
City
Tucson
State
AZ
Country
United States
Zip Code
85721
Peña, Edsel A; Wu, Wensong; Piegorsch, Walter et al. (2017) Model Selection and Estimation with Quantal-Response Data in Benchmark Risk Assessment. Risk Anal 37:716-732
Piegorsch, Walter W; Xiong, Hui; Bhattacharya, Rabi N et al. (2014) Benchmark Dose Analysis via Nonparametric Regression Modeling. Risk Anal 34:135-51
Piegorsch, Walter W; An, Lingling; Wickens, Alissa A et al. (2013) Information-theoretic model-averaged benchmark dose analysis in environmental risk assessment. Environmetrics 24:143-157
Bhattacharya, Rabi; Lin, Lizhen (2013) RECENT PROGRESS IN THE NONPARAMETRIC ESTIMATION OF MONOTONE CURVES -WITH APPLICATIONS TO BIOASSAY AND ENVIRONMENTAL RISK ASSESSMENT. Comput Stat Data Anal 63:63-80
Piegorsch, Walter W; Xiong, Hui; Bhattacharya, Rabi N et al. (2012) Nonparametric estimation of benchmark doses in environmental risk assessment. Environmetrics 23:717-728
West, R Webster; Piegorsch, Walter W; Peña, Edsel A et al. (2012) The Impact of Model Uncertainty on Benchmark Dose Estimation. Environmetrics 23:706-716
Deutsch, Roland C; Piegorsch, Walter W (2012) Benchmark dose profiles for joint-action quantal data in quantitative risk assessment. Biometrics 68:1313-22
Bhattacharya, Rabi; Lin, Lizhen (2011) NONPARAMETRIC BENCHMARK ANALYSIS IN RISK ASSESSMENT: A COMPARATIVE STUDY BY SIMULATION AND DATA ANALYSIS. Sankhya Ser B 73:144-163
Deutsch, Roland C; Grego, John M; Habing, Brian et al. (2010) Maximum likelihood estimation with binary-data regression models: small-sample and large-sample features. Adv Appl Stat 14:101-116
Piegorsch, Walter W (2010) Translational benchmark risk analysis. J Risk Res 13:653-667

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