Gottschalk Paul G, Dunn John R
Brendan Technologies, Inc., Carlsbad, CA 92008, USA.
Comput Methods Programs Biomed. 2005 Dec;80(3):204-15. doi: 10.1016/j.cmpb.2005.08.003. Epub 2005 Oct 26.
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.
本文描述了一种方法，该方法使用改进的蒙特卡罗方法来更准确地估计浓度估计值的置信区间。与将回归问题线性化并使用线性理论来获得置信区间这种常见但往往效果不佳的方法不同，此方法甚至适用于强非线性曲线模型，如五参数逻辑模型。该方法采用插值技术，以减少因模拟样本量小以及曲线模型中接近水平渐近线而在精密度分布图中产生的伪影。本文还介绍了如何通过确定误差大小（根据浓度置信区间的大小定义）超过应用所确定的可接受阈值时的浓度，来定义和计算剂量反应曲线的最小和最大可接受浓度。
