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Monday, September 06, 2010

DOSE-RESPONSE MODELS

 

The terminology dose-response model is commonly used to mean either a family of dose-response models or a specific member of the family of dose-response models.  The context usually identifies which meaning is applicable.  A family of dose-response models is a collection of models all with the same general parametric or functional form  (e.g., the family of all polynomials or the family of all straight lines).  A specific member of the family of dose-response models is one specific function with specific values given for all parameters (e.g., the straight line with intercept equal to zero and slope equal to one).

 

Some of the dose-response models that Sielken & Associates use and which are included in GEN.T include the following:

 

Linearized Multistage Model (Quantal Response Model)

Multistage Model (Quantal Response Model)

Weibull Model (Quantal Response Model)

Probit Model (Quantal Response Model)

Logit Model (Quantal Response Model)

Multihit Model (Quantal Response Model)

Multistage Model with Cumulative Dose (Quantal Response Model)

Armitage-Doll Model (Time-to-Response Model)

Two-Stage Growth Model (Time-to-Response Model)

            also known as the Moolgavkar-Venzon-Knudsen (MVK) Model

Generalized Hartley-Sielken Model (Time-to-Response Model)

Original Hartley-Sielken Model (Time-to-Response Model)

Gompertz-Makeham Model (Time-to-Response Model)

 

3. Human Health Risk Assessment
3.1     
Quantitative Risk Assessment and Statistical Analysis
3.2      Importance of Dose and Dose-Response Relationships
3.3      Misuse of Regulatory Upper-Bound Risk Characterizations
3.4      Risk Characterization Choices and Risk Exaggeration
3.5      A Better Approach to Cancer Risk Characterization
3.6      Overview of Background, Motivation, and Statistical Methods for Margin-of-Exposure Characterizations of Cancer Risks
           3.6.1    Importance of Dose
           3.6.2    Dose-Response Modeling
           3.6.3    Dose-Response Models
           3.6.4    Maximum Likelihood Estimation
           3.6.5    
Multistage Model
           3.6.6    Example of Fitted Multistage Model
           3.6.7    Potency
           3.6.8    Linearized Multistage Model
           3.6.9    Overstatement of Risks by the Linearized Multistage Model
           3.6.10  Adverse Impacts of the Variability in the Magnitude of the Bias in the Linearized Multistage Model's Overstatement of Risks
           3.6.11  Non-Responsiveness of the Linearized Multistage Model to Data
           3.6.12  Ranking Relative Risks
           3.6.13  Added Risk versus Extra Risk
           3.6.14  Need for a Better Dose-Response Characterization
           3.6.15  Better Dose-Response Characterization
           3.6.16  Benchmark Doses
           3.6.17  Responsiveness of Benchmark Doses Data Versus the Relative Non-Responsiveness of the Regulatory Upper-Bound Potency Q1* based on the Linearized Multistage Model
           3.6.18  Recommended Dose-Response Characterization
           3.6.19  Margin-of-Exposure Characterizations
           3.6.20  Conclusion
           3.6.21  Figures 1 to 16
3.7      Innovative Risk Assessment
3.8      Components of High-to-Low-Dose Extrapolation and Dose-Response Modeling
3.9      Probabilistic Exposure Assessment
3.10    Aggregate Risk Assessment
3.11    Cumulative Risk Assessment
3.12    Example Activities