Wang Shirley V, Schneeweiss Sebastian, Berger Marc L, Brown Jeffrey, de Vries Frank, Douglas Ian, Gagne Joshua J, Gini Rosa, Klungel Olaf, Mullins C Daniel, Nguyen Michael D, Rassen Jeremy A, Smeeth Liam, Sturkenboom Miriam
Division of Pharmacoepidemiology and Pharmacoeconomics, Brigham and Women's Hospital, MA, USA.
Department of Medicine, Harvard Medical School, MA, USA.
Pharmacoepidemiol Drug Saf. 2017 Sep;26(9):1018-1032. doi: 10.1002/pds.4295.
Defining a study population and creating an analytic dataset from longitudinal healthcare databases involves many decisions. Our objective was to catalogue scientific decisions underpinning study execution that should be reported to facilitate replication and enable assessment of validity of studies conducted in large healthcare databases.
We reviewed key investigator decisions required to operate a sample of macros and software tools designed to create and analyze analytic cohorts from longitudinal streams of healthcare data. A panel of academic, regulatory, and industry experts in healthcare database analytics discussed and added to this list.
Evidence generated from large healthcare encounter and reimbursement databases is increasingly being sought by decision-makers. Varied terminology is used around the world for the same concepts. Agreeing on terminology and which parameters from a large catalogue are the most essential to report for replicable research would improve transparency and facilitate assessment of validity. At a minimum, reporting for a database study should provide clarity regarding operational definitions for key temporal anchors and their relation to each other when creating the analytic dataset, accompanied by an attrition table and a design diagram. A substantial improvement in reproducibility, rigor and confidence in real world evidence generated from healthcare databases could be achieved with greater transparency about operational study parameters used to create analytic datasets from longitudinal healthcare databases.
从纵向医疗保健数据库中定义研究人群并创建分析数据集涉及许多决策。我们的目标是梳理出支撑研究实施的科学决策，这些决策应予以报告，以促进研究的可重复性，并有助于评估在大型医疗保健数据库中开展的研究的有效性。
我们审查了操作宏样本和软件工具所需的主要研究者决策，这些宏和软件工具旨在从纵向医疗保健数据流中创建和分析分析队列。一个由医疗保健数据库分析领域的学术、监管和行业专家组成的小组对这份清单进行了讨论并补充。
决策者越来越多地寻求从大型医疗保健就诊和报销数据库中生成的证据。世界各地对相同概念使用了不同的术语。就术语以及大型目录中的哪些参数对于可重复研究最为关键达成一致，将提高透明度并有助于评估有效性。至少，数据库研究报告应在创建分析数据集时，明确关键时间锚点的操作定义及其相互关系，并附带损耗表和设计图。通过提高用于从纵向医疗保健数据库创建分析数据集的操作研究参数的透明度，可以在医疗保健数据库生成的真实世界证据的可重复性、严谨性和可信度方面取得实质性改进。
