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处理缺失或“有问题”的药理学数据的方法:药代动力学。

Approaches to handling missing or "problematic" pharmacology data: Pharmacokinetics.

机构信息

Division of Pharmaceutics and Pharmacology, College of Pharmacy, The Ohio State University, Columbus, OH, USA.

Department of Pharmaceutical Sciences, Skaggs School of Pharmacy and Pharmaceutical Sciences, University of Colorado Anschutz Medical Campus, Aurora, CO, USA.

出版信息

CPT Pharmacometrics Syst Pharmacol. 2021 Apr;10(4):291-308. doi: 10.1002/psp4.12611.

Abstract

Missing or erroneous information is a common problem in the analysis of pharmacokinetic (PK) data. This may present as missing or inaccurate dose level or dose time, drug concentrations below the analytical limit of quantification, missing sample times, or missing or incorrect covariate information. Several methods to handle problematic data have been evaluated, although no single, broad set of recommendations for commonly occurring errors has been published. In this tutorial, we review the existing literature and present the results of our simulation studies that evaluated common methods to handle known data errors to bridge the remaining gaps and expand on the existing knowledge. This tutorial is intended for any scientist analyzing a PK data set with missing or apparently erroneous data. The approaches described herein may also be useful for the analysis of nonclinical PK data.

摘要

在药代动力学(PK)数据分析中,缺失或错误的信息是一个常见的问题。这可能表现为缺失或不准确的剂量水平或剂量时间、低于定量分析下限的药物浓度、缺失的样本时间,或缺失或不正确的协变量信息。已经评估了几种处理有问题数据的方法,尽管尚未公布针对常见错误的单一、广泛的建议。在本教程中,我们回顾了现有文献,并介绍了我们的模拟研究结果,这些研究评估了处理已知数据错误的常用方法,以弥合剩余的差距并扩展现有知识。本教程适用于任何分析具有缺失或明显错误数据的 PK 数据集的科学家。本文所述的方法对于非临床 PK 数据的分析也可能有用。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9212/8099444/f182eceb7cd2/PSP4-10-291-g003.jpg

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