Schneeweiss Sebastian, Glynn Robert J
The authors are from the Division of Pharmacoepidemiology, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA. Dr. Schneeweiss's research that contributed to this work is funded by grants and contracts from the Patient Center Outcomes Research Institute, the National Institutes of Health, the U.S. Food & Drug Administration. Disclosures - Dr. Schneeweiss is a principal investigator of research contracts from Genentech, Inc. and Boehringer Ingelheim to Brigham and Women's Hospital from which he receives a salary. He is a consultant to WHISCON, LLC and Aetion, Inc., of which he holds equity. The current paper is closely adapted from the prior work of the authors.
Am J Law Med. 2018 May;44(2-3):197-217. doi: 10.1177/0098858818789429.
Healthcare database analyses (claims, electronic health records) have been identified by various regulatory initiatives, including the 21 Century Cures Act and Prescription Drug User Fee Act ("PDUFA"), as useful supplements to randomized clinical trials to generate evidence on the effectiveness, harm, and value of medical products in routine care. Specific applications include accelerated drug approval pathways and secondary indications for approved medical products. Such real-world data ("RWD") analyses reflect how medical products impact health outside a highly controlled research environment. A constant stream of data from the routine operation of modern healthcare systems that can be analyzed in rapid cycles enables incremental evidence development for regulatory decision-making. Key evidentiary needs by regulators include 1) monitoring of medication performance in routine care, including the effectiveness, safety and value; 2) identifying new patient strata in which a drug may have added value or unacceptable harms; and 3) monitoring targeted utilization. Four broad requirements have been proposed to enable successful regulatory decision-making based on healthcare database analyses (collectively, "MVET"): Meaningful evidence that provides relevant and context-informed evidence sufficient for interpretation, drawing conclusions, and making decisions; valid evidence that meets scientific and technical quality standards to allow causal interpretations; expedited evidence that provides incremental evidence that is synchronized with the decision-making process; and transparent evidence that is audible, reproducible, robust, and ultimately trusted by decision-makers. Evidence generation systems that satisfy MVET requirements to a high degree will contribute to effective regulatory decision-making. Rapid-cycle analytics of healthcare databases is maturing at a time when regulatory overhaul increasingly demands such evidence. Governance, regulations, and data quality are catching up as the utility of this resource is demonstrated in multiple contexts.
医疗保健数据库分析（索赔数据、电子健康记录）已被包括《21世纪治愈法案》和《处方药使用者付费法案》（“PDUFA”）在内的各种监管举措认定为随机临床试验的有用补充，以生成关于医疗产品在常规护理中的有效性、危害和价值的证据。具体应用包括加速药物批准途径和已批准医疗产品的次要适应症。此类真实世界数据（“RWD”）分析反映了医疗产品在高度受控的研究环境之外如何影响健康。来自现代医疗保健系统日常运营的源源不断的数据可以在快速周期内进行分析，从而为监管决策提供渐进式的证据开发。监管机构的关键证据需求包括：1）监测常规护理中的药物性能，包括有效性、安全性和价值；2）识别药物可能具有附加价值或不可接受危害的新患者群体；3）监测目标使用情况。为了基于医疗保健数据库分析成功进行监管决策，已经提出了四项广泛要求（统称为“MVET”）：有意义的证据，提供足以进行解释、得出结论和做出决策的相关且有背景信息的证据；有效的证据，符合科学和技术质量标准以允许进行因果解释；快速的证据，提供与决策过程同步的渐进式证据；以及透明的证据，是可听的、可重复的、可靠的，最终为决策者所信任。高度满足MVET要求的证据生成系统将有助于有效的监管决策。在监管改革日益需要此类证据之际，医疗保健数据库的快速周期分析正在成熟。随着这种资源的效用在多个背景下得到证明，治理、法规和数据质量也在不断跟进。
