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Implications of the principle of question propagation for comparative-effectiveness and 'data mining' research.

机译:问题传播原理对比较有效性和“数据挖掘”研究的启示。

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摘要

RECENT LEGISLATION INCORPORATED COMPARATIVE-effectiveness research (CER) as a scientific mechanism to help improve health care.1 The law expresses particular interest in discovering which treatments work "in a real world setting" and encourages conduct of observational studies using data mining techniques of standardized electronic records.1-2 Ideally, CER will identify effective interventions in the subgroup of patients, since traditional randomized trials typically provide efficacy data for an "average" patient only.12 It is likely that the amount of observational research will increase significantly, especially studies involving data mining of large administrative databases and electronic medical records. However, epistemological arguments suggest that data mining efforts cannot provide definitive answers to the questions asked by the CER program. Rather, CER should be considered hypothesis-generating research aiming to inform future prospective studies that will invariably require new (and better) data collection.
机译:近期立法将比较效果研究(CER)作为一种有助于改善医疗保健的科学机制。1该法律特别关注发现哪种治疗在“现实世界中”有效,并鼓励使用标准化的数据挖掘技术进行观察性研究。电子记录1-2。理想情况下,CER将在患者亚组中确定有效的干预措施,因为传统的随机试验通常仅提供“平均”患者的疗效数据。12观察性研究的数量可能会大大增加,尤其是研究涉及大型行政数据库和电子病历的数据挖掘。但是,认识论观点认为,数据挖掘工作无法为CER计划提出的问题提供确定的答案。相反,应将CER视为假设研究,旨在为将来的前瞻性研究提供信息,这些研究总是需要新的(更好的)数据收集。

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