Kim Hun-Sung
Department of Medical Informatics, College of Medicine, The Catholic University of Korea, Seoul, Korea.
Division of Endocrinology and Metabolism, Department of Internal Medicine, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Korea.
J Korean Med Sci. 2024 Mar 11;39(9):e92. doi: 10.3346/jkms.2024.39.e92.
Randomized controlled trials (RCTs) and real-world evidence (RWE) studies are crucial and complementary in generating clinical evidence. RCTs provide controlled settings to validate the clinical effect of specific drugs or medical devices, while RWE integrates extrinsic factors, encompassing external influences affecting real-world scenarios, thus challenging RCT results in practical applications. In this study, we explore the impact of extrinsic factors on RWE outcomes, focusing on "dark data," which refers to data collected but not used or excluded from the analyses. Dark data can arise in many ways during research process, from selecting study samples to data collection and analysis. However, even unused or unanalyzed dark data hold potential insights, providing a comprehensive view of clinical contexts. Extrinsic factors lead to divergent RWE outcomes that could differ from RCTs beyond statistical correction's scope. Two main types of dark data exist: "known-unknown" and "unknown-unknown." The distinction between these dark data types highlights RWE's complexity. The transformation of into depends on data literacy-powerful utilization capabilities that can be interpreted based on medical expertise. Shifting the focus to excluded subjects or unused data in real-world contexts reveals unexplored potential. Understanding the significance of dark data is vital in reflecting the complexity of clinical settings. Connecting RCTs and RWEs requires medical data literacy, enabling clinicians to decipher meaningful insights. In the big data and artificial intelligence era, medical staff must navigate data complexities while promoting the core role of medicine. Prepared clinicians will lead this transformative journey, ensuring data value shapes the medical landscape.
随机对照试验（RCT）和真实世界证据（RWE）研究在生成临床证据方面至关重要且相辅相成。随机对照试验提供可控环境以验证特定药物或医疗器械的临床效果，而真实世界证据整合了外部因素，包括影响现实世界情况的外部影响，从而在实际应用中对随机对照试验的结果提出挑战。在本研究中，我们探讨外部因素对真实世界证据结果的影响，重点关注“暗数据”，即收集但未使用或从分析中排除的数据。暗数据可能在研究过程中的许多环节出现，从选择研究样本到数据收集和分析。然而，即使是未使用或未分析的暗数据也蕴含潜在的见解，能提供临床背景的全面视图。外部因素导致真实世界证据结果出现分歧，这种分歧可能超出统计校正范围，与随机对照试验结果不同。暗数据主要有两种类型：“已知 - 未知”和“未知 - 未知”。这些暗数据类型之间的区别凸显了真实世界证据的复杂性。从 到 的转变取决于数据素养——强大的利用能力，可基于医学专业知识进行解读。将重点转向现实世界中被排除的受试者或未使用的数据，揭示了未被探索的潜力。理解暗数据的重要性对于反映临床环境的复杂性至关重要。连接随机对照试验和真实世界证据需要医学数据素养，使临床医生能够解读有意义的见解。在大数据和人工智能时代，医务人员必须应对数据复杂性，同时发挥医学的核心作用。有准备的临床医生将引领这一变革之旅，确保数据价值塑造医疗格局。
