Determining the error of dose estimates and minimum and maximum acceptable concentrations from assays with nonlinear dose–response curves
| Autor: | John R. Dunn, Paul G. Gottschalk |
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| Rok vydání: | 2005 |
| Předmět: |
Health Informatics
Models Biological Sensitivity and Specificity Statistics Confidence Intervals Computer Simulation Mathematics Immunoassay Models Statistical Dose-Response Relationship Drug Linear system Reproducibility of Results Contrast (statistics) Confidence interval Computer Science Applications Nonlinear system Nonlinear Dynamics Pharmaceutical Preparations Sample size determination Data Interpretation Statistical Sample Size Maximum Allowable Concentration Asymptote Monte Carlo Method Unit-weighted regression Algorithms Software Interpolation |
| Zdroj: | Computer Methods and Programs in Biomedicine. 80:204-215 |
| ISSN: | 0169-2607 |
| DOI: | 10.1016/j.cmpb.2005.08.003 |
| Popis: | A method is described here that uses a modified Monte-Carlo method to provide an improved estimate of the confidence bounds of concentration estimates. This method accommodates even strongly nonlinear curve models, such as the five parameter logistic model, in contrast to the common but often poor approach of linearizing the regression problem and using linear theory to obtain the confidence bounds. The method uses an interpolation technique to reduce artifacts in the precision profile due to small simulation sample sizes and proximity to horizontal asymptotes in the curve model. The paper also describes how to define and calculate the minimum and maximum acceptable concentrations of dose-response curves by locating the concentrations where the size of the error, defined in terms of the size of the concentration confidence interval, exceeds the threshold of acceptability determined for the application. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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