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已分析样本的再分析:是否需要更改样本量计算?

Incurred Sample Reanalysis: Time to Change the Sample Size Calculation?

机构信息

Pharmacokinetics Department, Pharmaceutical Research Institute, 8 Rydygiera Street, 01-793, Warsaw, Poland.

Faculty of Mathematics and Information Science, Warsaw University of Technology, 75 Koszykowa Street, 00-662, Warsaw, Poland.

出版信息

AAPS J. 2019 Feb 11;21(2):28. doi: 10.1208/s12248-019-0293-2.

Abstract

Reliable results of pharmacokinetic and toxicokinetic studies are vital for correct decision making during drug discovery and development. Thus, ensuring high quality of bioanalytical methods is of critical importance. Incurred sample reanalysis (ISR)-one of the tools used to validate a method-is included in the bioanalytical regulatory recommendations. The methodology of this test is well established, but the estimation of the sample size is still commented on and contested. We have applied the hypergeometric distribution to evaluate ISR test passing rates in different clinical study sizes. We have tested both fixed rates of the clinical samples-as currently recommended by FDA and EMA-and a fixed number of ISRs. Our study revealed that the passing rate using the current sample size calculation is related to the clinical study size. However, the passing rate is much less dependent on the clinical study size when a fixed number of ISRs is used. Thus, we suggest using a fixed number of ISRs, e.g., 30 samples, for all studies. We found the hypergeometric distribution to be an adequate model for the assessment of similarities in original and repeated data. This model may be further used to optimize the sample size needed for the ISR test as well as to bridge data from different methods. This paper provides a basis to re-consider current ISR recommendations and implement a more statistically rationalized and risk-controlled approach.

摘要

药代动力学和毒代动力学研究的可靠结果对于药物发现和开发过程中的正确决策至关重要。因此,确保生物分析方法的高质量至关重要。重现性分析(ISR)是用于验证方法的工具之一,已被纳入生物分析监管建议中。该测试的方法学已经得到很好的确立,但对于样本量的估计仍存在争议。我们应用超几何分布来评估不同临床研究规模下 ISR 测试的通过率。我们同时测试了目前 FDA 和 EMA 推荐的固定临床样本比例和固定数量的 ISR。我们的研究表明,使用当前样本量计算的通过率与临床研究规模有关。然而,当使用固定数量的 ISR 时,通过率对临床研究规模的依赖性要小得多。因此,我们建议在所有研究中使用固定数量的 ISR,例如 30 个样本。我们发现超几何分布是评估原始数据和重复数据之间相似性的合适模型。该模型可进一步用于优化 ISR 测试所需的样本量,并桥接来自不同方法的数据。本文为重新考虑当前的 ISR 建议并实施更具统计学合理性和风险控制的方法提供了依据。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3a6c/6373415/eefc7d540d4a/12248_2019_293_Fig1_HTML.jpg

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