Maelstrom Research guidelines for rigorous retrospective data harmonization.
Maelstrom Research guidelines for rigorous retrospective data harmonization.
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DOI：
10.1093/ije/dyw075
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发表时间：
2017-02-01
影响因子：
7.7
通讯作者：
Burton P
中科院分区：
文献类型：
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作者：
Fortier I;Raina P;Van den Heuvel ER;Griffith LE;Craig C;Saliba M;Doiron D;Stolk RP;Knoppers BM;Ferretti V;Granda P;Burton P
背景：人们广泛接受并承认数据协调是至关重要的：如果没有数据协调，对现有高质量数据的主要部分的共同分析容易效率低下或出错。然而，尽管其广泛的实践，没有正式的/系统的指导方针存在，以确保高质量的回顾性数据协调。方法：为了更好地了解现实世界的协调实践并促进正式指南的制定，2006年至2015年期间开展了三项相关举措。其中包括对34项主要国际研究计划的电话调查，与专家举行的一系列研讨会，以及应用拟议指导方针的案例研究。结果：广泛的项目使用回顾性协调来支持他们的研究活动，但即使使用了适当的方法，所采用的术语、程序、技术和方法也有很大差异。本文概述的通用指南概述了所需的要点，并描述了一种相互依存的逐步协调方法：0)定义研究问题、目标和方案；1)收集已有的知识并选择研究；2)确定目标变量，评估协调潜力；3)工艺数据；4)估计生成的协调数据集的质量；5)传播和保存最终的协调产品。结论：这份手稿提供了指导方针，旨在鼓励严格和有效的协调方法，这些方法是全面和透明的文件，并且易于解释和实施。这可以被视为朝着实施指导原则迈出的关键一步，类似于那些被公认为确保系统评价和临床试验荟萃分析的基础基础所必需的指导原则。
Background: It is widely accepted and acknowledged that data harmonization is crucial: in its absence, the co-analysis of major tranches of high quality extant data is liable to inefficiency or error. However, despite its widespread practice, no formalized/systematic guidelines exist to ensure high quality retrospective data harmonization. Methods: To better understand real-world harmonization practices and facilitate development of formal guidelines, three interrelated initiatives were undertaken between 2006 and 2015. They included a phone survey with 34 major international research initiatives, a series of workshops with experts, and case studies applying the proposed guidelines. Results: A wide range of projects use retrospective harmonization to support their research activities but even when appropriate approaches are used, the terminologies, procedures, technologies and methods adopted vary markedly. The generic guidelines outlined in this article delineate the essentials required and describe an interdependent step-by-step approach to harmonization: 0) define the research question, objectives and protocol; 1) assemble pre-existing knowledge and select studies; 2) define targeted variables and evaluate harmonization potential; 3) process data; 4) estimate quality of the harmonized dataset(s) generated; and 5) disseminate and preserve final harmonization products. Conclusions: This manuscript provides guidelines aiming to encourage rigorous and effective approaches to harmonization which are comprehensively and transparently documented and straightforward to interpret and implement. This can be seen as a key step towards implementing guiding principles analogous to those that are well recognised as being essential in securing the foundational underpinning of systematic reviews and the meta-analysis of clinical trials.