关于药物相互作用试验的样本量计算。

IF 1.3 4区 医学 Q4 PHARMACOLOGY & PHARMACY
Pharmaceutical Statistics Pub Date : 2024-07-01 Epub Date: 2024-02-14 DOI:10.1002/pst.2367
Paul Meyvisch, Mitra Ebrahimpoor
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引用次数: 0

摘要

药物相互作用(DDI)试验是药物开发的重要组成部分,因为它们提供了两种或两种以上药物同时服用时的益处和风险证据。样本量的计算通常建议以临床上合理的无效应界限为基础,但这些界限在实际操作中很难界定,而默认的 0.8-1.25 无效应界限又过于保守,需要较大的样本量。此外,当已有药理学证据表明两种药物之间可能存在轻度或中度相互作用时,无效应界限就没有什么用处了,在这种情况下,效应界限会更有用。我们引入了基于精确度的样本量计算方法,既考虑了药代动力学参数的随机性,又考虑了(无)效应界限(如果存在)的预期宽度。这种方法简单明了,所需的样本量少得多,而且具有良好的操作特性。本文通过一个关于他汀类药物的案例研究来说明这一观点。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
On sample size calculation in drug interaction trials.

Drug-drug interaction (DDI) trials are an important part of drug development as they provide evidence on the benefits and risks when two or more drugs are taken concomitantly. Sample size calculation is typically recommended to be based on the existence of clinically justified no-effect boundaries but these are challenging to define in practice, while the default no-effect boundaries of 0.8-1.25 are known to be overly conservative requiring a large sample size. In addition, no-effect boundaries are of little use when there is prior pharmacological evidence that a mild or moderate interaction between two drugs may be present, in which case effect boundaries would be more useful. We introduce precision-based sample size calculation that accounts for both the stochastic nature of the pharmacokinetic parameters and the anticipated width of (no-)effect boundaries, should these exist. The methodology is straightforward, requires considerably less sample size and has favorable operating characteristics. A case study on statins is presented to illustrate the ideas.

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来源期刊
Pharmaceutical Statistics
Pharmaceutical Statistics 医学-统计学与概率论
CiteScore
2.70
自引率
6.70%
发文量
90
审稿时长
6-12 weeks
期刊介绍: Pharmaceutical Statistics is an industry-led initiative, tackling real problems in statistical applications. The Journal publishes papers that share experiences in the practical application of statistics within the pharmaceutical industry. It covers all aspects of pharmaceutical statistical applications from discovery, through pre-clinical development, clinical development, post-marketing surveillance, consumer health, production, epidemiology, and health economics. The Journal is both international and multidisciplinary. It includes high quality practical papers, case studies and review papers.
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