Determining the error of dose estimates and minimum and maximum acceptable concentrations from assays with nonlinear dose-response curves.
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.
Gottschalk
Paul G
PG
Brendan Technologies, Inc., Carlsbad, CA 92008, USA.
Dunn
John R
JR
D000465:Algorithms; D003198:Computer Simulation; D016001:Confidence Intervals; D003627:Data Interpretation, Statistical; D004305:Dose-Response Relationship, Drug; D007118:Immunoassay / Q000379:methods*; D008452:Maximum Allowable Concentration; D008954:Models, Biological; D015233:Models, Statistical; D009010:Monte Carlo Method; D017711:Nonlinear Dynamics; D004364:Pharmaceutical Preparations; D015203:Reproducibility of Results; D018401:Sample Size; D012680:Sensitivity and Specificity
D004364:Pharmaceutical Preparations