Department of Applied and Computational Mathematics and Statistics, University of Notre Dame, 46530, Notre Dame, IN, USA.
School of Health Sciences and Education, Harokopio University, Athens, Greece.
BMC Med Res Methodol. 2022 Nov 5;22(1):287. doi: 10.1186/s12874-022-01768-6.
The increased adoption of the internet, social media, wearable devices, e-health services, and other technology-driven services in medicine and healthcare has led to the rapid generation of various types of digital data, providing a valuable data source beyond the confines of traditional clinical trials, epidemiological studies, and lab-based experiments.
We provide a brief overview on the type and sources of real-world data and the common models and approaches to utilize and analyze real-world data. We discuss the challenges and opportunities of using real-world data for evidence-based decision making This review does not aim to be comprehensive or cover all aspects of the intriguing topic on RWD (from both the research and practical perspectives) but serves as a primer and provides useful sources for readers who interested in this topic.
Real-world hold great potential for generating real-world evidence for designing and conducting confirmatory trials and answering questions that may not be addressed otherwise. The voluminosity and complexity of real-world data also call for development of more appropriate, sophisticated, and innovative data processing and analysis techniques while maintaining scientific rigor in research findings, and attentions to data ethics to harness the power of real-world data.
互联网、社交媒体、可穿戴设备、电子健康服务和其他医学和医疗保健领域的技术驱动服务的广泛采用，导致各种类型的数字数据的快速生成，提供了超出传统临床试验、流行病学研究和基于实验室实验范围的宝贵数据源。
我们简要概述了真实世界数据的类型和来源，以及利用和分析真实世界数据的常见模型和方法。我们讨论了利用真实世界数据进行循证决策的挑战和机遇。本综述的目的不是全面或涵盖真实世界数据（从研究和实践角度）这个有趣主题的所有方面，而是作为入门读物，为有兴趣了解该主题的读者提供有用的资源。
真实世界数据为设计和进行确证性试验以及回答其他方法可能无法解决的问题提供了真实世界证据的巨大潜力。真实世界数据的庞大规模和复杂性也要求开发更合适、复杂和创新的数据处理和分析技术，同时在研究结果中保持科学严谨性，并关注数据伦理，以利用真实世界数据的力量。
