Ueckert Sebastian, Karlsson Mats O, Hooker Andrew C
Pharmacometrics Research Group, Department of Pharmaceutical Biosciences, Uppsala University, P.O. Box 591, 751 24, Uppsala, Sweden.
J Pharmacokinet Pharmacodyn. 2016 Apr;43(2):223-34. doi: 10.1007/s10928-016-9468-y. Epub 2016 Mar 2.
Estimating the power for a non-linear mixed-effects model-based analysis is challenging due to the lack of a closed form analytic expression. Often, computationally intensive Monte Carlo studies need to be employed to evaluate the power of a planned experiment. This is especially time consuming if full power versus sample size curves are to be obtained. A novel parametric power estimation (PPE) algorithm utilizing the theoretical distribution of the alternative hypothesis is presented in this work. The PPE algorithm estimates the unknown non-centrality parameter in the theoretical distribution from a limited number of Monte Carlo simulation and estimations. The estimated parameter linearly scales with study size allowing a quick generation of the full power versus study size curve. A comparison of the PPE with the classical, purely Monte Carlo-based power estimation (MCPE) algorithm for five diverse pharmacometric models showed an excellent agreement between both algorithms, with a low bias of less than 1.2 % and higher precision for the PPE. The power extrapolated from a specific study size was in a very good agreement with power curves obtained with the MCPE algorithm. PPE represents a promising approach to accelerate the power calculation for non-linear mixed effect models.
由于缺乏封闭形式的解析表达式，估计基于非线性混合效应模型的分析效能具有挑战性。通常，需要采用计算密集型的蒙特卡罗研究来评估计划实验的效能。如果要获得完整的效能与样本量曲线，这尤其耗时。本文提出了一种利用备择假设理论分布的新型参数化效能估计（PPE）算法。PPE算法通过有限数量的蒙特卡罗模拟和估计来估计理论分布中未知的非中心参数。估计参数与研究规模呈线性比例关系，从而能够快速生成完整的效能与研究规模曲线。将PPE与针对五种不同药代动力学模型的经典纯蒙特卡罗效能估计（MCPE）算法进行比较，结果表明两种算法之间具有出色的一致性，PPE的偏差小于1.2%，精度更高。从特定研究规模外推得到的效能与通过MCPE算法获得的效能曲线非常吻合。PPE是一种加速非线性混合效应模型效能计算的有前景的方法。
