Largajolli Anna, Bertoldo Alessandra, Cobelli Claudio
Department of Information Engineering, University of Padova, Via G. Gradenigo 6/B, 35131 Padova, Italy.
Annu Int Conf IEEE Eng Med Biol Soc. 2012;2012:5482-5. doi: 10.1109/EMBC.2012.6347235.
The nonlinear mixed effects models (NLMEM) are widespread modeling techniques in PKPD analysis and epidemiological studies because they can produce a description of not only the individual but also of the population features. Moreover, they are able to deal with individual data sparseness by borrowing the lack of information from the entire population. In this way, the NLMEM do not fail where instead other techniques, such as the traditional individual weighted least squares (WLS), sometimes do. The NLME approach relies on the maximization of a likelihood function that due to model parametric nonlinearity not always has an explicit solution. Various techniques have been proposed to solve this problem including the first order (FO) and the first order conditional (FOCE) estimation methods that approximate the likelihood function through a linearization; the expectation maximization algorithm (EM) that maximize the exact likelihood; the Bayesian estimation method where a third stage of variability, the distribution of the population parameters, is taken into account [1]. Recently, new estimation methods that rely on the EM algorithm have been implemented in the last release of the population software NONMEM [2]. These methods are: the iterative two stage (ITS), Monte Carlo importance sampling EM (IMP), Monte Carlo importance sampling EM assisted by Mode a Posteriori estimation (IMPMAP) and the Stochastic Approximation EM (SAEM). Moreover, another new method is available, the Markov Chain Monte Carlo Bayesian Analysis (BAYES), next to the more known FO and FOCE. With this article we want to complete the Denti et al [3] simulation study by evaluating the newest population methods applied on the IVGTT glucose minimal model.
非线性混合效应模型（NLMEM）是药代动力学-药效学（PKPD）分析和流行病学研究中广泛应用的建模技术，因为它们不仅可以描述个体特征，还能描述群体特征。此外，它们能够通过利用整个群体的信息来处理个体数据稀疏的问题。通过这种方式，NLMEM在其他技术（如传统的个体加权最小二乘法（WLS））有时会失败的情况下不会失败。NLME方法依赖于似然函数的最大化，由于模型参数的非线性，该似然函数并不总是有显式解。已经提出了各种技术来解决这个问题，包括通过线性化近似似然函数的一阶（FO）和一阶条件（FOCE）估计方法；最大化精确似然的期望最大化算法（EM）；考虑群体参数分布这一第三变异性阶段的贝叶斯估计方法[1]。最近，依赖于EM算法的新估计方法已在群体软件NONMEM的最新版本中实现[2]。这些方法包括：迭代两阶段法（ITS）、蒙特卡罗重要性抽样EM（IMP）、后验模式辅助蒙特卡罗重要性抽样EM（IMPMAP）和随机近似EM（SAEM）。此外，除了更为人所知的FO和FOCE之外，还有另一种新方法可用，即马尔可夫链蒙特卡罗贝叶斯分析（BAYES）。通过本文，我们希望通过评估应用于静脉葡萄糖耐量试验（IVGTT）葡萄糖最小模型的最新群体方法来完成Denti等人[3]的模拟研究。
