Departments of Food Safety and Food Quality, Ghent University, Ghent, Belgium.
Molecular Epidemiology Research Group, Max Delbrück Centre for Molecular Medicine, Berlin, Germany.
Adv Nutr. 2017 Sep 15;8(5):639-651. doi: 10.3945/an.117.015651. Print 2017 Sep.
Pooled analysis of secondary data increases the power of research and enables scientific discovery in nutritional epidemiology. Information on study characteristics that determine data quality is needed to enable correct reuse and interpretation of data. This study aims to define essential quality characteristics for data from observational studies in nutrition. First, a literature review was performed to get an insight on existing instruments that assess the quality of cohort, case-control, and cross-sectional studies and dietary measurement. Second, 2 face-to-face workshops were organized to determine the study characteristics that affect data quality. Third, consensus on the data descriptors and controlled vocabulary was obtained. From 4884 papers retrieved, 26 relevant instruments, containing 164 characteristics for study design and 93 characteristics for measurements, were selected. The workshop and consensus process resulted in 10 descriptors allocated to "study design" and 22 to "measurement" domains. Data descriptors were organized as an ordinal scale of items to facilitate the identification, storage, and querying of nutrition data. Further integration of an Ontology for Nutrition Studies will facilitate interoperability of data repositories.
二次数据分析可提高研究的效能,促进营养流行病学领域的科学发现。为了正确地重复使用和解释数据,需要了解决定数据质量的研究特征信息。本研究旨在确定营养领域观察性研究中数据的基本质量特征。首先,进行文献综述,以了解评估队列研究、病例对照研究和横断面研究以及膳食测量质量的现有工具。其次,组织了 2 次面对面研讨会,以确定影响数据质量的研究特征。第三,就数据描述符和受控词汇达成了共识。从检索到的 4884 篇论文中,选择了 26 种相关工具,其中包含 164 项研究设计特征和 93 项测量特征。研讨会和共识过程确定了 10 个分配给“研究设计”和 22 个分配给“测量”领域的描述符。数据描述符组织为一个有序的项目量表,便于识别、存储和查询营养数据。进一步整合营养研究本体论将促进数据存储库的互操作性。