Combining Real-World and Clinical Trial Data Through Privacy-Preserving Record Linkage: Opportunities and Challenges-A Narrative Review

通过保护隐私的记录链接整合真实世界数据和临床试验数据:机遇与挑战——叙述性综述

阅读:1

Abstract

BACKGROUND AND AIMS: Despite their widespread use, randomized clinical trials (RCTs) face challenges like differential loss to follow-up, which can impact validity. Real-world evidence (RWE) from real-world data (RWD) is increasingly used to address these limitations, but RCTs and RWE have provided complementary, disconnected observations of the patient journey. Privacy-preserving record linkage (PPRL) enables the integration of patient records across these data sources. This narrative review explores the potential use cases of PPRL to overcome the limitations of both RCTs and RWD for clinical research and regulatory decision-making. METHODS: This manuscript is a narrative review and did not involve the collection or analysis of primary research data. The authors aimed for comprehensive topic coverage and a synthesis of key concepts from the current literature, rather than adhering to a formal systematic review protocol (e.g., PRISMA). RESULTS: PPRL can generate a more comprehensive understanding of patient interaction with the healthcare system. For example, long-term information about participants before and after a trial can assist in identifying predictors of drug response or intolerance, reducing patient burden, and providing alternatives to traditional study designs. Linked data applications include expanding patient health histories and creating comprehensive patient data repositories that enable innovative trial designs. However, opportunities remain to demonstrate the provenance, quality, and completeness of RWD sources to ensure scientific rigor. CONCLUSION: Combining RCTs and RWD through PPRL offers significant and insufficiently explored potential for advancing drug development research, reducing operational costs, and enhancing data availability. Further consideration of PPRL use cases may drive innovative trial designs augmented with RWD, improving the ability of this collected data to support informed decision-making.

特别声明

1、本页面内容包含部分的内容是基于公开信息的合理引用;引用内容仅为补充信息,不代表本站立场。

2、若认为本页面引用内容涉及侵权,请及时与本站联系,我们将第一时间处理。

3、其他媒体/个人如需使用本页面原创内容,需注明“来源:[生知库]”并获得授权;使用引用内容的,需自行联系原作者获得许可。

4、投稿及合作请联系:info@biocloudy.com。