1School of Population Health, RRCSI University of Medicine and Health Sciences, Dublin, Ireland.
2Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, U.K.
Diabetes Care. 2023 Jul 1;46(7):1316-1326. doi: 10.2337/dc22-1438.
The past decade of population research for diabetes has seen a dramatic proliferation of the use of real-world data (RWD) and real-world evidence (RWE) generation from non-research settings, including both health and non-health sources, to influence decisions related to optimal diabetes care. A common attribute of these new data is that they were not collected for research purposes yet have the potential to enrich the information around the characteristics of individuals, risk factors, interventions, and health effects. This has expanded the role of subdisciplines like comparative effectiveness research and precision medicine, new quasi-experimental study designs, new research platforms like distributed data networks, and new analytic approaches for clinical prediction of prognosis or treatment response. The result of these developments is a greater potential to progress diabetes treatment and prevention through the increasing range of populations, interventions, outcomes, and settings that can be efficiently examined. However, this proliferation also carries an increased threat of bias and misleading findings. The level of evidence that may be derived from RWD is ultimately a function of the data quality and the rigorous application of study design and analysis. This report reviews the current landscape and applications of RWD in clinical effectiveness and population health research for diabetes and summarizes opportunities and best practices in the conduct, reporting, and dissemination of RWD to optimize its value and limit its drawbacks.
在过去的十年中，糖尿病的人口研究见证了真实世界数据（RWD）和真实世界证据（RWE）的大量使用，这些数据来自非研究环境，包括健康和非健康来源，以影响与最佳糖尿病护理相关的决策。这些新数据的一个共同特点是，它们不是为研究目的而收集的，但有可能丰富有关个体特征、风险因素、干预措施和健康影响的信息。这扩展了诸如比较效果研究和精准医学等子学科的作用、新的准实验研究设计、分布式数据网络等新的研究平台，以及用于临床预后或治疗反应预测的新分析方法。这些发展的结果是，通过能够更有效地检查越来越多的人群、干预措施、结果和环境，糖尿病治疗和预防的潜力得到了更大的提升。然而，这种扩散也带来了更大的偏见和误导性发现的威胁。从 RWD 中获得的证据水平最终取决于数据质量以及研究设计和分析的严格应用。本报告回顾了 RWD 在糖尿病临床效果和人群健康研究中的当前应用，并总结了在 RWD 的实施、报告和传播方面的机会和最佳实践，以优化其价值并限制其缺点。
