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J Pharmacokinet Pharmacodyn. 2012 Feb;39(1):17-35. doi: 10.1007/s10928-011-9228-y. Epub 2011 Nov 19.
The paper compares performance of Nonmem estimation methods--first order conditional estimation with interaction (FOCEI), iterative two stage (ITS), Monte Carlo importance sampling (IMP), importance sampling assisted by mode a posteriori (IMPMAP), stochastic approximation expectation-maximization (SAEM), and Markov chain Monte Carlo Bayesian (BAYES), on the simulated examples of a monoclonal antibody with target-mediated drug disposition (TMDD), demonstrates how optimization of the estimation options improves performance, and compares standard errors of Nonmem parameter estimates with those predicted by PFIM 3.2 optimal design software. In the examples of the one- and two-target quasi-steady-state TMDD models with rich sampling, the parameter estimates and standard errors of the new Nonmem 7.2.0 ITS, IMP, IMPMAP, SAEM and BAYES estimation methods were similar to the FOCEI method, although larger deviation from the true parameter values (those used to simulate the data) was observed using the BAYES method for poorly identifiable parameters. Standard errors of the parameter estimates were in general agreement with the PFIM 3.2 predictions. The ITS, IMP, and IMPMAP methods with the convergence tester were the fastest methods, reducing the computation time by about ten times relative to the FOCEI method. Use of lower computational precision requirements for the FOCEI method reduced the estimation time by 3-5 times without compromising the quality of the parameter estimates, and equaled or exceeded the speed of the SAEM and BAYES methods. Use of parallel computations with 4-12 processors running on the same computer improved the speed proportionally to the number of processors with the efficiency (for 12 processor run) in the range of 85-95% for all methods except BAYES, which had parallelization efficiency of about 70%.
本文比较了非参数估计方法的性能——具有相互作用的一阶条件估计（FOCEI）、迭代两阶段（ITS）、蒙特卡罗重要抽样（IMP）、后验模式辅助重要抽样（IMPMAP）、随机逼近期望最大化（SAEM）和贝叶斯马尔可夫链蒙特卡罗（BAYES），针对具有靶向介导药物处置（TMDD）的单克隆抗体的模拟实例，展示了如何优化估计选项以提高性能，并比较了非参数估计参数的标准误差与 PFIM 3.2 最佳设计软件预测的标准误差。在具有丰富采样的单靶和双靶准稳态 TMDD 模型的实例中，新的非参数 7.2.0 ITS、IMP、IMPMAP、SAEM 和 BAYES 估计方法的参数估计和标准误差与 FOCEI 方法相似，尽管使用 BAYES 方法对难以识别的参数，观察到与真实参数值（用于模拟数据）的较大偏差。参数估计的标准误差通常与 PFIM 3.2 的预测值一致。带有收敛检验器的 ITS、IMP 和 IMPMAP 方法是最快的方法，与 FOCEI 方法相比，计算时间减少了约十倍。FOCEI 方法使用较低的计算精度要求将估计时间缩短了 3-5 倍，而不会影响参数估计的质量，并等于或超过 SAEM 和 BAYES 方法的速度。使用相同计算机上的 4-12 个处理器进行并行计算，所有方法的速度都按处理器数量成比例提高，效率（对于 12 个处理器运行）在 85-95%之间，除了 BAYES 方法，其并行化效率约为 70%。
