神经眼科学中的大数据研究：前景与挑战。
Big Data Research in Neuro-Ophthalmology: Promises and Pitfalls.
机构信息
Departments of Ophthalmology and Neurology & Neurological Sciences (HEM), Stanford University, Palo Alto, California; Departments of Ophthalmology and Visual Sciences (CEJ), University of Illinois, School of Public Health, College of Medicine, Epidemiology and Public Health, Chicago, Illinois; Department of Anesthesia and Critical Care (DSR), University of Chicago, Chicago, Illinois; and Departments of Anesthesiology, Ophthalmology and Visual Sciences (SR), College of Medicine, University of Illinois, Chicago, Illinois.
出版信息
J Neuroophthalmol. 2019 Dec;39(4):480-486. doi: 10.1097/WNO.0000000000000751.
BACKGROUND
Big data clinical research involves application of large data sets to the study of disease. It is of interest to neuro-ophthalmologists but also may be a challenge because of the relative rarity of many of the diseases treated.
EVIDENCE ACQUISITION
Evidence for this review was gathered from the authors' experiences performing analysis of large data sets and review of the literature.
RESULTS
Big data sets are heterogeneous, and include prospective surveys, medical administrative and claims data and registries compiled from medical records. High-quality studies must pay careful attention to aspects of data set selection, including potential bias, and data management issues, such as missing data, variable definition, and statistical modeling to generate appropriate conclusions. There are many studies of neuro-ophthalmic diseases that use big data approaches.
CONCLUSIONS
Big data clinical research studies complement other research methodologies to advance our understanding of human disease. A rigorous and careful approach to data set selection, data management, data analysis, and data interpretation characterizes high-quality studies.
背景
大数据临床研究涉及将大型数据集应用于疾病研究。神经眼科医生对此很感兴趣，但由于所治疗的许多疾病相对罕见，这也可能是一个挑战。
证据获取
本综述的证据来自作者对大型数据集进行分析的经验以及对文献的回顾。
结果
大数据集是异质的，包括前瞻性调查、医疗行政和索赔数据以及从病历中汇编的登记册。高质量的研究必须仔细注意数据集选择的各个方面，包括潜在的偏差，以及数据管理问题，如数据缺失、变量定义和统计建模，以得出适当的结论。有许多使用大数据方法研究神经眼科疾病的研究。
结论
大数据临床研究补充了其他研究方法，以加深我们对人类疾病的理解。严格而仔细的数据集选择、数据管理、数据分析和数据解释方法是高质量研究的特征。
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