Glucose Minimal Model population analysis: likelihood function profiling via Monte Carlo sampling.
Glucose Minimal Model population analysis: likelihood function profiling via Monte Carlo sampling.
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葡萄糖最小模型总体分析：通过蒙特卡罗抽样进行似然函数分析。
DOI：
10.1109/iembs.2008.4650320
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发表时间：
2008
期刊：
影响因子：
--
通讯作者：
Cobelli,Claudio
中科院分区：
文献类型：
--
作者：
Denti,Paolo;Vicini,Paolo;Bertoldo,Alessandra;Cobelli,Claudio
种群动力学建模方法以非线性混合效应模型的形式实现，由于其在从稀疏采样数据中估计种群特征方面的价值，在许多生物医学领域引起了越来越多的兴趣。然而，它们的应用通常需要原始模型函数的近似值，其效果通常难以衡量。我们应用负对数似然分析来评估模型近似对葡萄糖-胰岛素最小模型的影响，并将非线性混合效应近似方法与两阶段方法进行比较。我们的初步研究结果表明，非线性混合效应模型提供了准确的参数估计，但也指出这种估计的可靠性可能受到大群体变异性和小样本量的影响。
Population kinetic modeling approaches, implemented as nonlinear mixed effects models, are attracting growing interest in many fields of biomedicine thanks to their value in estimating population features from sparsely sampled data. However, their application often entails approximations of the original model function, whose effect is difficult to gauge in general. We apply negative log-likelihood profiling to assess the effect of model approximation on the glucose-insulin Minimal Model, and compare nonlinear mixed-effects approximate methods to two-stage methods. Our preliminary findings suggest that nonlinear mixed effects models provide accurate parameter estimates, but also point out that the reliability of such estimates may be affected by large population variability and small sample size.