用于临床试验中集成临床和转化数据管理的开源SQL数据库模式。

IF 2.2 3区 医学 Q3 MEDICINE, RESEARCH & EXPERIMENTAL Clinical Trials Pub Date : 2024-12-25 DOI:10.1177/17407745241304331
Umar Niazi, Charlotte Stuart, Patricia Soares, Vincent Foure, Gareth Griffiths
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引用次数: 0

摘要

在肿瘤学中释放个性化医疗的力量取决于临床试验数据与转化数据(即生物样本衍生的分子信息)的整合。这种综合分析使研究人员能够根据患者独特的生物构成定制治疗方案。然而,目前英国临床试验单位的实践存在挑战。虽然临床数据以标准化格式保存,但转译数据复杂多样,需要专门存储。这种格式上的差异给旨在有效地管理、整合和分析这些数据集的研究人员造成了重大障碍。本文提出了一个新颖的解决方案:一个专门为学术试用单位的需要而设计的开源SQL数据库模式。受英国癌症研究中心对开放数据共享的承诺的启发,并以南安普顿临床试验单位的CONFIRM试验(超过150,000个临床数据点)为例,该模式在原始数据和昂贵的安全数据环境/可信研究环境之间提供了一个具有成本效益和实用的“中间地带”。通过充当临床和转译数据的中心枢纽,该模式促进了无缝的数据共享和分析。研究人员获得试验的整体观点,使探索临床观察和治疗反应的分子基础之间的联系成为可能。提供了设置数据库的详细说明。开源特性和简单的设计确保了易于实现和负担得起,同时强大的安全措施保护敏感数据。我们进一步展示了研究人员如何利用流行的统计软件,如R,直接查询数据库。这种方法促进了学术发现社区的合作,最终加速了个性化癌症治疗的进展。
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An open-source SQL database schema for integrated clinical and translational data management in clinical trials.

Unlocking the power of personalised medicine in oncology hinges on the integration of clinical trial data with translational data (i.e. biospecimen-derived molecular information). This combined analysis allows researchers to tailor treatments to a patient's unique biological makeup. However, current practices within UK Clinical Trials Units present challenges. While clinical data are held in standardised formats, translational data are complex, diverse, and requires specialised storage. This disparity in format creates significant hurdles for researchers aiming to curate, integrate and analyse these datasets effectively. This article proposes a novel solution: an open-source SQL database schema designed specifically for the needs of academic trial units. Inspired by Cancer Research UK's commitment to open data sharing and exemplified by the Southampton Clinical Trials Unit's CONFIRM trial (with over 150,000 clinical data points), this schema offers a cost-effective and practical 'middle ground' between raw data and expensive Secure Data Environments/Trusted Research Environments. By acting as a central hub for both clinical and translational data, the schema facilitates seamless data sharing and analysis. Researchers gain a holistic view of trials, enabling exploration of connections between clinical observations and the molecular underpinnings of treatment response. Detailed instructions for setting up the database are provided. The open-source nature and straightforward design ensure ease of implementation and affordability, while robust security measures safeguard sensitive data. We further showcase how researchers can leverage popular statistical software like R to directly query the database. This approach fosters collaboration within the academic discovery community, ultimately accelerating progress towards personalised cancer therapies.

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来源期刊
Clinical Trials
Clinical Trials 医学-医学:研究与实验
CiteScore
4.10
自引率
3.70%
发文量
82
审稿时长
6-12 weeks
期刊介绍: Clinical Trials is dedicated to advancing knowledge on the design and conduct of clinical trials related research methodologies. Covering the design, conduct, analysis, synthesis and evaluation of key methodologies, the journal remains on the cusp of the latest topics, including ethics, regulation and policy impact.
期刊最新文献
Evaluating the use of text-message reminders and personalised text-message reminders on the return of participant questionnaires in trials, a systematic review and meta-analysis. Impact of differences between interim and post-interim analysis populations on outcomes of a group sequential trial: Example of the MOVe-OUT study. From RAGs to riches: Utilizing large language models to write documents for clinical trials. Hybrid sample size calculations for cluster randomised trials using assurance. Characterization of studies considered and required under Medicare's coverage with evidence development program.
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