PDF(Pubmed)
Abstract：
Data extraction (DE) is a challenging step in systematic reviews (SRs). Complex SRs can involve multiple interventions and/or outcomes and encompass multiple research questions. Attempts have been made to clarify DE aspects focusing on the subsequent meta-analysis; there are, however, no guidelines for DE in complex SRs. Comparing datasets extracted independently by pairs of reviewers to detect discrepancies is also cumbersome, especially when the number of extracted variables and/or studies is colossal. This work aims to provide a set of practical steps to help SR teams design and build DE tools and compare extracted data for complex SRs.
We provided a 10-step guideline, from determining data items and structure to data comparison, to help identify discrepancies and solve data disagreements between reviewers. The steps were organised into three phases: planning and building the database and data manipulation. Each step was described and illustrated with examples, and relevant references were provided for further guidance. A demonstration example was presented to illustrate the application of Epi Info and R in the database building and data manipulation phases. The proposed guideline was also summarised and compared with previous DE guidelines.
The steps of this guideline are described generally without focusing on a particular software application or meta-analysis technique. We emphasised determining the organisational data structure and highlighted its role in the subsequent steps of database building. In addition to the minimal programming skills needed, creating relational databases and data validation features of Epi info can be utilised to build DE tools for complex SRs. However, two R libraries are needed to facilitate data comparison and solve discrepancies.
We hope adopting this guideline can help review teams construct DE tools that suit their complex review projects. Although Epi Info depends on proprietary software for data storage, it can still be a potential alternative to other commercial DE software for completing complex reviews.
We provided a 10-step guideline, from determining data items and structure to data comparison, to help identify discrepancies and solve data disagreements between reviewers. The steps were organised into three phases: planning and building the database and data manipulation. Each step was described and illustrated with examples, and relevant references were provided for further guidance. A demonstration example was presented to illustrate the application of Epi Info and R in the database building and data manipulation phases. The proposed guideline was also summarised and compared with previous DE guidelines.
The steps of this guideline are described generally without focusing on a particular software application or meta-analysis technique. We emphasised determining the organisational data structure and highlighted its role in the subsequent steps of database building. In addition to the minimal programming skills needed, creating relational databases and data validation features of Epi info can be utilised to build DE tools for complex SRs. However, two R libraries are needed to facilitate data comparison and solve discrepancies.
We hope adopting this guideline can help review teams construct DE tools that suit their complex review projects. Although Epi Info depends on proprietary software for data storage, it can still be a potential alternative to other commercial DE software for completing complex reviews.
摘要：
背景：数据提取（DE）是系统综述（SRs）中具有挑战性的步骤。复杂的SR可能涉及多种干预措施和/或结果，并包含多个研究问题。已经尝试澄清侧重于随后的荟萃分析的DE方面；有，然而,在复杂的SRs中没有DE指南。比较成对的审阅者独立提取的数据集以检测差异也很麻烦，特别是当提取的变量和/或研究的数量是巨大的。这项工作旨在提供一组实际步骤，以帮助SR团队设计和构建DE工具，并比较复杂SR的提取数据。
方法：我们提供了10步指南，从确定数据项和结构到数据比较，以帮助识别差异并解决审阅者之间的数据分歧。这些步骤分为三个阶段：规划和构建数据库以及数据操作。每个步骤都用例子描述和说明，并为进一步指导提供了相关参考。给出了一个演示示例，以说明EpiInfo和R在数据库构建和数据操作阶段的应用。还总结了拟议的指南，并与以前的DE指南进行了比较。
结果：本指南的步骤是一般性描述的，而不关注特定的软件应用程序或荟萃分析技术。我们强调确定组织数据结构，并强调其在数据库构建的后续步骤中的作用。除了所需的最低限度的编程技能，创建Epiinfo的关系数据库和数据验证功能可用于为复杂的SR构建DE工具。然而,需要两个R库，以方便数据比较和解决差异。
结论：我们希望采用本指南可以帮助评审团队构建适合其复杂评审项目的DE工具。尽管EpiInfo依赖于专有软件进行数据存储，它仍然可以替代其他商业DE软件来完成复杂的审查。
方法：我们提供了10步指南，从确定数据项和结构到数据比较，以帮助识别差异并解决审阅者之间的数据分歧。这些步骤分为三个阶段：规划和构建数据库以及数据操作。每个步骤都用例子描述和说明，并为进一步指导提供了相关参考。给出了一个演示示例，以说明EpiInfo和R在数据库构建和数据操作阶段的应用。还总结了拟议的指南，并与以前的DE指南进行了比较。
结果：本指南的步骤是一般性描述的，而不关注特定的软件应用程序或荟萃分析技术。我们强调确定组织数据结构，并强调其在数据库构建的后续步骤中的作用。除了所需的最低限度的编程技能，创建Epiinfo的关系数据库和数据验证功能可用于为复杂的SR构建DE工具。然而,需要两个R库，以方便数据比较和解决差异。
结论：我们希望采用本指南可以帮助评审团队构建适合其复杂评审项目的DE工具。尽管EpiInfo依赖于专有软件进行数据存储，它仍然可以替代其他商业DE软件来完成复杂的审查。
