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一种评估临床试验的向量理论:在生物等效性中的应用。

A Vector Theory of Assessing Clinical Trials: An Application to Bioequivalence.

作者信息

Karalis Vangelis D

机构信息

Department of Pharmacy, School of Health Sciences, National and Kapodistrian University of Athens, 15784 Athens, Greece.

Institute of Applied and Computational Mathematics, Foundation for Research and Technology Hellas (FORTH), 70013 Heraklion, Greece.

出版信息

J Cardiovasc Dev Dis. 2024 Jun 21;11(7):185. doi: 10.3390/jcdd11070185.

DOI:10.3390/jcdd11070185
PMID:39057608
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11277341/
Abstract

A novel idea is introduced regarding the statistical comparisons of endpoints in clinical trials. Currently, the (dis)similarity of measured endpoints is not assessed. Instead, statistical analysis is directly applied, which can lead to multiplicity issues, reduced statistical power, and the recruitment of more subjects. The Vector-Based Comparison (VBC) approach originates from vector algebra and considers clinical endpoints as "vectors". In the general case of N clinical endpoints, a Cartesian coordinate system is defined, and the most important primary endpoint (E1) is set. Following an explicitly defined procedure, the pairwise relationships of the remaining N-1 endpoints with E1 are estimated, and the N-1 endpoints are decomposed into axes perpendicular to E1. The angle between vectors provides insight into the level of dependency between variables. Vectors that are perpendicular to each other are considered independent, and only these are used in the statistical analysis. In this work, VBC is applied to bioequivalence studies of three anti-hypertensive drugs: amlodipine, irbesartan, and hydrochlorothiazide. The results suggest that VBC is a reproducible, easily applicable method allowing for the discrimination and utilization of the endpoint component expressing different attributes. All clinical characteristics are assessed with increased statistical power, without inflation of type I error.

摘要

本文介绍了一种关于临床试验中终点指标统计比较的新方法。目前,尚未对测量终点指标的(不)相似性进行评估。取而代之的是直接进行统计分析,这可能会导致多重性问题、统计功效降低以及需要招募更多受试者。基于向量的比较(VBC)方法源自向量代数,并将临床终点指标视为“向量”。在有N个临床终点指标的一般情况下,定义一个笛卡尔坐标系,并设定最重要的主要终点指标(E1)。按照明确规定的程序,估计其余N - 1个终点指标与E1的两两关系,并将这N - 1个终点指标分解为垂直于E1的轴。向量之间的夹角可以洞察变量之间的依赖程度。相互垂直的向量被视为独立向量,并且仅将这些向量用于统计分析。在本研究中,VBC被应用于三种抗高血压药物(氨氯地平、厄贝沙坦和氢氯噻嗪)的生物等效性研究。结果表明,VBC是一种可重复、易于应用的方法,能够区分和利用表达不同属性的终点指标成分。所有临床特征都通过提高统计功效进行评估,而不会增加I型错误。

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本文引用的文献

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Sample size calculation in clinical trials with two co-primary endpoints including overdispersed count and continuous outcomes.临床试验中两个主要终点(包括过离散计数和连续结局)的样本量计算。
Pharm Stat. 2024 Jan-Feb;23(1):46-59. doi: 10.1002/pst.2337. Epub 2023 Sep 7.
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Finite sample corrections for average equivalence testing.有限样本下平均等效性检验的校正。
Stat Med. 2024 Feb 28;43(5):833-854. doi: 10.1002/sim.9993. Epub 2023 Dec 19.
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From bench to bedside, 2-in-1 design expedites phase 2/3 oncology drug development.从实验室到临床,二合一设计加速了2/3期肿瘤药物研发。
Front Oncol. 2023 Oct 9;13:1251672. doi: 10.3389/fonc.2023.1251672. eCollection 2023.
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An In Vitro-In Vivo Simulation Approach for the Prediction of Bioequivalence.一种用于预测生物等效性的体外-体内模拟方法。
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6
The optimal design of clinical trials with potential biomarker effects: A novel computational approach.具有潜在生物标志物效应的临床试验的最优设计:一种新的计算方法。
Stat Med. 2021 Mar 30;40(7):1752-1766. doi: 10.1002/sim.8868. Epub 2021 Jan 11.
7
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Ther Innov Regul Sci. 2020 May;54(3):528-533. doi: 10.1007/s43441-019-00084-4. Epub 2020 Jan 6.
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IEEE Rev Biomed Eng. 2021;14:116-126. doi: 10.1109/RBME.2020.3007816. Epub 2021 Jan 22.
9
Multiplicity Adjustment and Sample Size Calculation in Clinical Trials with Multiple Endpoints: An Industry Survey of Current Practices in Japan.临床试验中多重终点的多重调整和样本量计算:日本行业当前实践的调查。
Ther Innov Regul Sci. 2020 Sep;54(5):1097-1105. doi: 10.1007/s43441-020-00126-2. Epub 2020 Feb 6.
10
Multiplicity Considerations in Clinical Trials.临床试验中的多重性考量
N Engl J Med. 2018 May 31;378(22):2115-2122. doi: 10.1056/NEJMra1709701.