使用关联规则挖掘分析韩国临床试验中的合作。

IF 1.1 Q4 PHARMACOLOGY & PHARMACY
Translational and Clinical Pharmacology Pub Date : 2024-12-01 Epub Date: 2024-12-16 DOI:10.12793/tcp.2024.32.e17
Ki Young Huh, Ildae Song
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引用次数: 0

摘要

确定试验地点如何协作对于多中心试验至关重要。根据研究阶段和临床试验领域的不同,建立试验点之间合作的方式可能有所不同。在本研究中,我们使用关联规则挖掘来揭示试验协作。我们使用了韩国食品药品安全部提供的试验批准数据,并组织了试验地点。我们收集了2012 - 2023年的试验信息,并根据研究阶段和临床试验领域对试验进行了分类。我们基于研究阶段和临床试验领域进行关联规则挖掘。我们确定了209个有效的试验点,并分析了在此期间进行的11,107项临床试验。从研究阶段来看,1期试验数量最多(5451项),其次是3期(2492项),其他(1826项)和2期(1338项)。我们发现1期临床试验有最高的提升指标。1期试验的平均升力为5.40,显著高于2期(1.68)和3期试验(1.72)。此外,第一阶段试验的试验合作网络结构高度浓缩,几个试验地点位于首尔和京畿道。不同临床试验领域的试验协作特征不同,儿科提升指标的平均值和可变性最高。综上所述,关联规则挖掘可以识别试验站点之间的协作。与其他阶段相比,第一阶段试验中的合作相对更具排他性,而且不同临床试验领域的合作各不相同。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Analyzing collaborations in clinical trials in Korea using association rule mining.

Identifying how trial sites collaborate is essential for multicenter trials. The ways in which collaboration among trial sites is established can vary according to study phase and clinical trial domains. In this study, we employed association rule mining to reveal trial collaboration. We used trial approval data provided by the Ministry of Food and Drug Safety in Korea and organized the trial sites. We collected trial information from 2012 to 2023 and categorized the trials according to study phase and clinical trial domain. We performed association rule mining based on study phase and clinical trial domain. We identified 209 valid trial sites and analyzed 11,107 clinical trials conducted during this period. By study phase, phase 1 trials accounted for the largest number (5,451), followed by phase 3 (2,492), others (1,826), and phase 2 (1,338). We found that phase 1 clinical trials had the highest lift metrics. The mean lift for phase 1 trials was 5.40, which was significantly greater than that of phase 2 (1.68) and phase 3 trials (1.72). Additionally, the network structure for trial collaboration in phase 1 trials was highly condensed, with several trial sites located in Seoul and Gyeonggi-do. Different trial collaboration characteristics were noted among clinical trial domains, with mean and variability of the lift metrics for pediatrics being the highest. In conclusion, association rule mining can identify collaborations among trial sites. Collaboration in phase 1 trials is relatively more exclusive than in other phases, and aspects of collaboration differ among clinical trial domains.

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来源期刊
Translational and Clinical Pharmacology
Translational and Clinical Pharmacology Medicine-Pharmacology (medical)
CiteScore
1.60
自引率
11.10%
发文量
17
期刊介绍: Translational and Clinical Pharmacology (Transl Clin Pharmacol, TCP) is the official journal of the Korean Society for Clinical Pharmacology and Therapeutics (KSCPT). TCP is an interdisciplinary journal devoted to the dissemination of knowledge relating to all aspects of translational and clinical pharmacology. The categories for publication include pharmacokinetics (PK) and drug disposition, drug metabolism, pharmacodynamics (PD), clinical trials and design issues, pharmacogenomics and pharmacogenetics, pharmacometrics, pharmacoepidemiology, pharmacovigilence, and human pharmacology. Studies involving animal models, pharmacological characterization, and clinical trials are appropriate for consideration.
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