Centre for Applied Pharmacokinetic Research, The University of Manchester, UK.
J Pharmacokinet Pharmacodyn. 2010 Feb;37(1):67-83. doi: 10.1007/s10928-009-9144-6. Epub 2009 Dec 5.
Population pharmacodynamic experiments sometime involve repeated measurements of ordinal random variables at specific time points. Such longitudinal data presents a challenge during modelling due to correlation between measurements within an individual and often mixed-effects modelling approach may be used for the analysis. It is important that these studies are adequately powered by including an adequate number of subjects in order to detect a significant treatment effect. This paper describes a method for calculating sample size for repeated ordinal measurements in population pharmacodynamic experiments based on analysis by a mixed-effects modelling approach. The Wald test is used for testing the significance of treatment effects. This method is fast, simple and efficient. It can also be extended to account for differential allocation of subjects to the groups and unbalanced sampling designs between and within groups. The results obtained from two simulation studies using nonlinear mixed-effects modelling software (NONMEM) showed good agreement between the power obtained from simulation and nominal power used for sample size calculations.
人群药效动力学实验有时涉及在特定时间点对有序随机变量进行重复测量。由于个体内测量之间存在相关性，此类纵向数据在建模过程中带来了挑战，通常会采用混合效应建模方法进行分析。为了检测到显著的治疗效果，通过纳入足够数量的受试者来充分为这些研究提供效能是很重要的。本文描述了一种基于混合效应建模方法分析的人群药效动力学实验中重复有序测量的样本量计算方法。Wald 检验用于检验治疗效果的显著性。该方法快速、简单、高效。它还可以扩展到考虑受试者在组间的差异分配和组内的不均衡采样设计。使用非线性混合效应建模软件 (NONMEM) 进行的两项模拟研究的结果表明，模拟获得的效能与用于样本量计算的名义效能之间具有良好的一致性。
