Bailer A J, Portier C J
Department of Mathematics and Statistics, Miami University, Oxford, OH 45056.
J Appl Toxicol. 1990 Aug;10(4):303-6. doi: 10.1002/jat.2550100413.
Drug concentrations in one-compartment systems are frequently modeled using a single exponential function. Two methods of estimation are commonly used for determining the parameters of such a model. In the first method, non-linear least-squares regression is used to calculate the parameters. In the second method, the data are first transformed by a logarithmic function, and then the log-concentration data are fit using linear least-squares regression. The assumptions for fitting these models are discussed with special emphasis on which data points are most influential in determining parameter values. The similarities between fitting a linear regression model to the log-concentration data and fitting a weighted regression model to the original data are noted. An example is presented that illustrates the differences in fitting a model to the log-transformed data versus fitting unweighted and weighted models to the original-scale data.
单室系统中的药物浓度通常使用单一指数函数进行建模。通常使用两种估计方法来确定此类模型的参数。在第一种方法中，使用非线性最小二乘回归来计算参数。在第二种方法中，数据首先通过对数函数进行变换，然后使用线性最小二乘回归对对数浓度数据进行拟合。讨论了拟合这些模型的假设，并特别强调了哪些数据点在确定参数值时最具影响力。指出了将线性回归模型拟合到对数浓度数据与将加权回归模型拟合到原始数据之间的相似性。给出了一个示例，说明了将模型拟合到对数变换后的数据与将未加权和加权模型拟合到原始尺度数据之间的差异。
