Department of Clinical Pharmacy, Institute of Pharmacy, University of Hamburg, Bundesstraße 45, 20146, Hamburg, Germany.
J Pharmacokinet Pharmacodyn. 2020 Jun;47(3):219-228. doi: 10.1007/s10928-020-09682-4. Epub 2020 Apr 4.
Assessing parameter uncertainty is a crucial step in pharmacometric workflows. Small datasets with ten or fewer subjects appear regularly in drug development and therapeutic use, but it is unclear which method to assess parameter uncertainty is preferable in such situations. The aim of this study was to (i) systematically evaluate the performance of standard error (SE), bootstrap (BS), log-likelihood profiling (LLP), Bayesian approaches (BAY) and sampling importance resampling (SIR) to assess parameter uncertainty in small datasets and (ii) to evaluate methods to provide proposal distributions for the SIR. A simulation study was conducted and the 0-95% confidence interval (CI) and coverage for each parameter was evaluated and compared to reference CIs derived by stochastic simulation and estimation (SSE). A newly proposed LLP-SIR, combining the proposal distribution provided by LLP with SIR, was included in addition to conventional SE-SIR and BS-SIR. Additionally, the methods were applied to a clinical dataset. The determined CIs differed substantially across the methods. The CIs of SE, BS, LLP and BAY were not in line with the reference in datasets with ≤ 10 subjects. The best alignment was found for the LLP-SIR, which also provided the best coverage results among the SIR methods. The best overall results regarding the coverage were provided by LLP and BAY across all parameters and dataset sizes. To conclude, the popular SE and BS methods are not suitable to derive parameter uncertainty in small datasets containing ≤ 10 subjects, while best performances were observed with LLP, BAY and LLP-SIR.
评估参数不确定性是药代动力学工作流程中的关键步骤。在药物开发和治疗应用中，经常会出现包含十个或更少受试者的小数据集，但尚不清楚在这种情况下评估参数不确定性的首选方法。本研究的目的是：（i）系统评估标准误差（SE）、自举（BS）、对数似然分布（LLP）、贝叶斯方法（BAY）和抽样重要性重采样（SIR）在小数据集中评估参数不确定性的性能，以及（ii）评估为 SIR 提供建议分布的方法。进行了一项模拟研究，评估并比较了每个参数的 0-95%置信区间（CI）和覆盖度与通过随机模拟和估计（SSE）得出的参考 CI。除了传统的 SE-SIR 和 BS-SIR 外，还纳入了一种新的 LLP-SIR，该方法将 LLP 提供的建议分布与 SIR 相结合。确定的 CI 在不同方法之间存在显著差异。在包含≤10 个受试者的数据集，SE、BS、LLP 和 BAY 的 CI 与参考值不一致。SIR 方法中， LLP-SIR 的一致性最好，同时也提供了最佳的覆盖结果。在所有参数和数据集大小中， LLP 和 BAY 提供了最佳的覆盖结果。总之，在包含≤10 个受试者的小数据集中，流行的 SE 和 BS 方法不适合用于推断参数不确定性，而 LLP、BAY 和 LLP-SIR 表现最佳。
