Martin I K, Weber K M, Ward G M, Best J D, Boston R C
Department of Chemistry and Biology, Footscray Institute of Technology, Vic., Australia.
Comput Methods Programs Biomed. 1990 Dec;33(4):193-203. doi: 10.1016/0169-2607(90)90070-p.
The minimal model approach to analysis of intravenous glucose tolerance tests (IVGTT) yields estimates of parameters representing insulin sensitivity, glucose-mediated glucose disposal and pancreatic responsiveness. The precision of these estimates can deteriorate if the glucose and insulin data lack well-defined structure or freedom from data noise (random error). The precision of parameter estimates can be enhanced if data sets from two or more IVGTTs, obtained under different experimental conditions in the same subject, are analysed together in one data file. Following initial fitting using CONSAM, the conversational version of the modeling program SAAM, those parameters whose estimates remain at the same value under the different experimental conditions are constrained. This effectively reduces the number of adjustable parameters, and their estimates can then be fine-tuned with enhanced precision using the batch version of SAAM.
静脉葡萄糖耐量试验（IVGTT）分析的最小模型方法可得出代表胰岛素敏感性、葡萄糖介导的葡萄糖处置和胰腺反应性的参数估计值。如果葡萄糖和胰岛素数据缺乏明确的结构或不受数据噪声（随机误差）影响，这些估计值的精度可能会降低。如果将同一受试者在不同实验条件下获得的两个或更多IVGTT的数据集在一个数据文件中一起分析，则可以提高参数估计的精度。使用建模程序SAAM的对话版本CONSAM进行初始拟合后，将那些在不同实验条件下估计值保持相同的参数进行约束。这有效地减少了可调整参数的数量，然后可以使用SAAM的批处理版本以更高的精度对其估计值进行微调。
