@文章{信息:doi/10.2196/29920，作者=“Babrak, Lmar Marie和Smakaj, Erand和Agac, Teyfik和Asprion, Petra Maria和Grimberg, Frank和der Werf, Daan Van和Van Ginkel, Erwin Willem和Tosoni, Deniz David和Clay, Ieuan和Degen, Markus和Brodbeck, Dominique和Natali, Eriberto Noel和Schkommodau, Erik和Miho, Enkelejda”，标题=“rws - cockpit:“真实世界数据质量评估申请”，期刊=“JMIR Form Res”，年份=“2022”，月份=“10月”，日=“18”，卷=“6”，数=“10”，页=“e29920”，关键词=“真实世界数据;真实的证据;质量评价;应用程序;背景:数字技术正在改变医疗保健系统。很大一部分信息是作为真实世界数据(RWD)生成的。来自电子健康记录和数字生物标记物的数据有可能揭示药物的益处和不良事件之间的关联，建立新的患者分层原则，揭示未知的疾病相关性，并为预防措施提供信息。对医疗保健支付者和提供者、生物制药行业和政府的影响在健康结果、医疗质量和成本方面是巨大的。然而，缺乏评估RWD初步质量的框架，从而阻碍了基于人群的观察性研究的开展，以支持监管决策和现实世界的证据。 Objective: To address the need to qualify RWD, we aimed to build a web application as a tool to translate characterization of some quality parameters of RWD into a metric and propose a standard framework for evaluating the quality of the RWD. Methods: The RWD-Cockpit systematically scores data sets based on proposed quality metrics and customizable variables chosen by the user. Sleep RWD generated de novo and publicly available data sets were used to validate the usability and applicability of the web application. The RWD quality score is based on the evaluation of 7 variables: manageability specifies access and publication status; complexity defines univariate, multivariate, and longitudinal data; sample size indicates the size of the sample or samples; privacy and liability stipulates privacy rules; accessibility specifies how the data set can be accessed and to what granularity; periodicity specifies how often the data set is updated; and standardization specifies whether the data set adheres to any specific technical or metadata standard. These variables are associated with several descriptors that define specific characteristics of the data set. Results: To address the need to qualify RWD, we built the RWD-Cockpit web application, which proposes a framework and applies a common standard for a preliminary evaluation of RWD quality across data sets---molecular, phenotypical, and social---and proposes a standard that can be further personalized by the community retaining an internal standard. Applied to 2 different case studies---de novo--generated sleep data and publicly available data sets---the RWD-Cockpit could identify and provide researchers with variables that might increase quality. Conclusions: The results from the application of the framework of RWD metrics implemented in the RWD-Cockpit application suggests that multiple data sets can be preliminarily evaluated in terms of quality using the proposed metrics. The output scores---quality identifiers---provide a first quality assessment for the use of RWD. Although extensive challenges remain to be addressed to set RWD quality standards, our proposal can serve as an initial blueprint for community efforts in the characterization of RWD quality for regulated settings. ", issn="2561-326X", doi="10.2196/29920", url="https://formative.www.mybigtv.com/2022/10/e29920", url="https://doi.org/10.2196/29920", url="http://www.ncbi.nlm.nih.gov/pubmed/35266872" }