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Principles of Large-scale Evidence Generation and Evaluation across a Network of Databases (LEGEND).
Journal of the American Medical Informatics Association ( IF 4.7 ) Pub Date : 2020-09-10 , DOI: 10.1093/jamia/ocaa103
Martijn J Schuemie 1, 2 , Patrick B Ryan 1, 3 , Nicole Pratt 4 , RuiJun Chen 3, 5 , Seng Chan You 6 , Harlan M Krumholz 7 , David Madigan 8 , George Hripcsak 3, 9 , Marc A Suchard 2, 10
Affiliation  

Evidence derived from existing health-care data, such as administrative claims and electronic health records, can fill evidence gaps in medicine. However, many claim such data cannot be used to estimate causal treatment effects because of the potential for observational study bias; for example, due to residual confounding. Other concerns include P hacking and publication bias.

中文翻译:


跨数据库网络的大规模证据生成和评估原则(LEGEND)。



来自现有医疗保健数据的证据,例如行政索赔和电子健康记录,可以填补医学领域的证据空白。然而,许多人声称这些数据不能用于估计因果治疗效果,因为观察性研究可能存在偏差;例如,由于残余混杂。其他担忧包括P黑客攻击和出版偏见。
更新日期:2020-09-10
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