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Multiplicity-adjusted inferences in risk assessment: benchmark analysis with quantal response data
Daniela K Nitcheva1, Walter W Piegorsch, R Webster West
1Department of Epidemiology and Biostatistics, University of South Carolina, Columbia, South Carolina 29208, USA. nitcheva@gwm.sc.edu
Biometrics
|March 2, 2005
Summary
Benchmark analysis helps estimate low-dose chemical risks using high-dose data. This study addresses multiplicity corrections for benchmark dose and risk calculations with quantal response data.
Area of Science:
- Toxicology
- Risk Assessment
- Biostatistics
Background:
- Quantitative risk assessment requires characterizing adverse effects from chemical or pharmaceutical agents.
- Low-dose data are often unavailable, necessitating inferences from high-dose exposure information.
Purpose of the Study:
- To discuss methods for applying multiplicity corrections in benchmark analysis.
- To address challenges in low-dose risk inference using quantal response data.
Main Methods:
- Benchmark analysis focusing on the dose achieving a fixed benchmark risk level.
- Calculation of upper confidence limits on risk and lower confidence limits on benchmark dose.
- Application of corrections for multiplicity when multiple benchmark risks are considered.
Main Results:
- Provides approaches for adjusting confidence limits for multiplicity in benchmark dose-risk assessment.
- Offers statistical methods for inferring low-dose effects from high-dose data.
Conclusions:
- The study presents practical approaches for multiplicity corrections in benchmark analysis.
- These methods enhance the reliability of low-dose risk and benchmark dose estimations in safety assessments.