Leveraging real-world data to investigate multiple sclerosis disease behavior, prognosis, and treatment
Leveraging real-world data to investigate multiple sclerosis disease behavior, prognosis, and treatment
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DOI：
10.1177/1352458519892555
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发表时间：
2019-11-28
影响因子：
5.8
通讯作者：
Marrie, Ruth Ann
中科院分区：
文献类型：
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作者：
Cohen, Jeffrey A.;Trojano, Maria;Marrie, Ruth Ann
随机对照临床试验和真实世界的观察性研究提供了补充信息，但有效性不同。一些临床问题（疾病行为、预后、结果测量的验证、比较有效性和治疗的长期安全性）通常可以使用反映更大、更有代表性的人群的真实世界数据来更好地解决。整合病史、临床医生报告的结果、性能测试和患者就诊期间患者报告的结果测量;成像和生物标本分析;以及来自可穿戴设备的数据，增加了数据集的实用性。然而，利用这些数据的观察性研究容易受到许多潜在偏倚来源的影响，从而对监管机构和医学界的接受造成障碍。因此，数据集内的数据标准化和验证、数据集之间的协调以及适当分析方法的应用是重要的考虑因素。我们回顾了改善真实世界数据的范围，质量和分析的方法，以促进对多发性硬化症及其治疗的理解，作为更好地支持患者护理和研究的机会的一个例子。
Randomized controlled clinical trials and real-world observational studies provide complementary information but with different validity. Some clinical questions (disease behavior, prognosis, validation of outcome measures, comparative effectiveness, and long-term safety of therapies) are often better addressed using real-world data reflecting larger, more representative populations. Integration of disease history, clinician-reported outcomes, performance tests, and patient-reported outcome measures during patient encounters; imaging and biospecimen analyses; and data from wearable devices increase dataset utility. However, observational studies utilizing these data are susceptible to many potential sources of bias, creating barriers to acceptance by regulatory agencies and the medical community. Therefore, data standardization and validation within datasets, harmonization across datasets, and application of appropriate analysis methods are important considerations. We review approaches to improve the scope, quality, and analyses of real-world data to advance understanding of multiple sclerosis and its treatment, as an example of opportunities to better support patient care and research.