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首页> 外文期刊>BMC Medical Research Methodology >Performance comparison of first-order conditional estimation with interaction and Bayesian estimation methods for estimating the population parameters and its distribution from data sets with a low number of subjects
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Performance comparison of first-order conditional estimation with interaction and Bayesian estimation methods for estimating the population parameters and its distribution from data sets with a low number of subjects

机译：估计人口参数估算的一阶条件估计的性能比较及其利用少量数据集的数据集分布

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Exploratory preclinical, as well as clinical trials, may involve a small number of patients, making it difficult to calculate and analyze the pharmacokinetic (PK) parameters, especially if the PK parameters show very high inter-individual variability (IIV). In this study, the performance of a classical first-order conditional estimation with interaction (FOCE-I) and expectation maximization (EM)-based Markov chain Monte Carlo Bayesian (BAYES) estimation methods were compared for estimating the population parameters and its distribution from data sets having a low number of subjects. In this study, 100 data sets were simulated with eight sampling points for each subject and with six different levels of IIV (5%, 10%, 20%, 30%, 50%, and 80%) in their PK parameter distribution. A stochastic simulation and estimation (SSE) study was performed to simultaneously simulate data sets and estimate the parameters using four different methods: FOCE-I only, BAYES(C) (FOCE-I and BAYES composite method), BAYES(F) (BAYES with all true initial parameters and fixed ω 2 ), and BAYES only. Relative root mean squared error (rRMSE) and relative estimation error (REE) were used to analyze the differences between true and estimated values. A case study was performed with a clinical data of theophylline available in NONMEM distribution media. NONMEM software assisted by Pirana, PsN, and Xpose was used to estimate population PK parameters, and R program was used to analyze and plot the results. The rRMSE and REE values of all parameter (fixed effect and random effect) estimates showed that all four methods performed equally at the lower IIV levels, while the FOCE-I method performed better than other EM-based methods at higher IIV levels (greater than 30%). In general, estimates of random-effect parameters showed significant bias and imprecision, irrespective of the estimation method used and the level of IIV. Similar performance of the estimation methods was observed with theophylline dataset. The classical FOCE-I method appeared to estimate the PK parameters more reliably than the BAYES method when using a simple model and data containing only a few subjects. EM-based estimation methods can be considered for adapting to the specific needs of a modeling project at later steps of modeling.
机译：探索性临床前，以及临床试验，可能涉及少数患者，使得难以计算和分析药代动力学（PK）参数，特别是如果PK参数显示出非常高的间间变异性（IIV）。在这项研究中，比较了与相互作用（FOCE-I）和期望最大化（EM）的经典一机条件估计的性能进行了比较，用于估计人口参数及其分布数据集具有较少数量的受试者。在本研究中，在PK参数分布中，用八个采样点模拟100个数据集，每个受试者的采样点，六种不同水平的IIV（5％，10％，20％，30％，50％和80％）。进行了随机仿真和估计（SSE）研究以同时模拟数据集并使用四种不同的方法估计参数：仅限Foce-i，贝叶斯（C）（Foce-I和Bayes Composite方法），贝叶斯（F）（贝叶斯所有真正的初始参数和固定ω2），只有贝叶斯。相对根均方误差（RRMSE）和相对估计误差（REE）用于分析真实和估计值之间的差异。使用非梅姆分布介质中可用的茶碱的临床数据进行案例研究。由Pirana，PSN和X波辅助的非梅尔姆软件用于估计人口PK参数，R程序用于分析和绘制结果。所有参数的RRMSE和REE值（固定效果和随机效应）估计表明，所有四种方法在较低的IIV水平上同样进行，而FOCE-I方法比IIV水平更高的基于其他基于EM的方法更好地执行（大于30％）。通常，无论使用的估计方法和IIV水平如何，随机效应参数的估计显示出显着的偏差和不精确。使用茶碱数据集观察估计方法的类似性能。经典的FOCE-I方法似乎更可靠地估计PK参数，而不是仅包含少数科目的简单模型和数据。可以考虑基于EM的估计方法，以适应建模在建模的稍后步骤中的建模项目的特定需求。

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