Statistical Power Calculations for Mixed Pharmacokinetic Study Designs Using a Population Approach
Statistical Power Calculations for Mixed Pharmacokinetic Study Designs Using a Population Approach
复制标题
DOI：
10.1208/s12248-014-9641-4
复制
发表时间：
2014-09-01
期刊：
影响因子：
4.5
通讯作者：
Tarning, Joel
中科院分区：
文献类型：
--
作者：
Kloprogge, Frank;Simpson, Julie A.;Tarning, Joel
同时建模密集和稀疏的药代动力学数据是可能的人口方法。为了确定检测协变量影响所需的个体数量，可以采用基于模拟的功率计算方法。蒙特卡罗映射功率方法（一种基于模拟的使用似然比检验的功率计算方法）在本研究中得到扩展，用于混合药代动力学研究（即稀疏和密集数据收集）的样本量计算。本分析使用了一个指导简单直接的药代动力学研究设计的工作流程，同时考虑了替代研究设计的成本效益。最初，对一种假设药物的数据进行了模拟，然后对抗疟疾药物双氢青蒿素进行了模拟。两个数据集（抽样设计A：密集；抽样设计B：稀疏）使用包含二元协变量效应的药代动力学模型进行模拟，随后使用(1)相同的模型和(2)NONMEM 7.2中不包含协变量效应的模型进行重新估计。对采用A和b两种抽样设计的不同数量的患者进行功率计算，选择统计功率为bbb80 %的研究设计，并进一步评估成本-效果。对假设药物和抗疟疾药物双氢青蒿素的模拟研究表明，基于蒙特卡罗映射功率方法的模拟功率计算方法可用于评估和确定混合（部分稀疏和部分密集抽样）研究设计的样本量。该方法有助于设计可靠、高效的药代动力学研究。
Simultaneous modelling of dense and sparse pharmacokinetic data is possible with a population approach. To determine the number of individuals required to detect the effect of a covariate, simulation-based power calculation methodologies can be employed. The Monte Carlo Mapped Power method (a simulation-based power calculation methodology using the likelihood ratio test) was extended in the current study to perform sample size calculations for mixed pharmacokinetic studies (i.e. both sparse and dense data collection). A workflow guiding an easy and straightforward pharmacokinetic study design, considering also the cost-effectiveness of alternative study designs, was used in this analysis. Initially, data were simulated for a hypothetical drug and then for the anti-malarial drug, dihydroartemisinin. Two datasets (sampling design A: dense; sampling design B: sparse) were simulated using a pharmacokinetic model that included a binary covariate effect and subsequently re-estimated using (1) the same model and (2) a model not including the covariate effect in NONMEM 7.2. Power calculations were performed for varying numbers of patients with sampling designs A and B. Study designs with statistical power > 80% were selected and further evaluated for cost-effectiveness. The simulation studies of the hypothetical drug and the anti-malarial drug dihydroartemisinin demonstrated that the simulation-based power calculation methodology, based on the Monte Carlo Mapped Power method, can be utilised to evaluate and determine the sample size of mixed (part sparsely and part densely sampled) study designs. The developed method can contribute to the design of robust and efficient pharmacokinetic studies.