Sheiner L B, Grasela T H
Drug Metab Rev. 1984;15(1-2):293-303. doi: 10.3109/03602538409015067.
NONMEM, a program package the produces the extended least squares estimates of population parameters for a nonlinear mixed-effect model, has been applied to two data sets from patients routinely receiving phenytoin. A general model for the data is proposed. The models used in previous, standard-method analyses of each data set are compared to the general model using NONMEM. The comparison involves two questions: The first asks whether the parameters estimated previously agree with NONMEM estimates when the original model is used. We find that for fixed-effect parameters they generally do, while for interindividual random-effect parameters the previous methods' estimates appear upward biased relative to NONMEM. Second, the original model per se is compared to the general model by comparing the best fit to each. The general model is clearly superior. NONMEM's ability to distinguish among models, and to precisely estimate their parameters from sparse individual data, is illustrated and verified.
NONMEM是一个程序包，用于生成非线性混合效应模型总体参数的扩展最小二乘估计值，已应用于常规接受苯妥英治疗患者的两个数据集。提出了一个适用于这些数据的通用模型。将之前对每个数据集进行标准方法分析时使用的模型与使用NONMEM的通用模型进行比较。该比较涉及两个问题：第一个问题是，当使用原始模型时，之前估计的参数是否与NONMEM估计值一致。我们发现，对于固定效应参数，通常是一致的，而对于个体间随机效应参数，之前方法的估计值相对于NONMEM似乎存在向上偏差。第二个问题是，通过比较各自的最佳拟合情况，将原始模型本身与通用模型进行比较。通用模型明显更优。展示并验证了NONMEM区分模型以及从稀疏个体数据精确估计其参数的能力。
