Department of Zoology, University of Cambridge, Cambridge, CB2 3QZ, UK.
BioRISC (Biosecurity Research Initiative at St Catharine's), St Catharine's College, Cambridge, CB2 1RL, UK.
BMC Biol. 2021 Feb 17;19(1):33. doi: 10.1186/s12915-021-00974-w.
Meta-analysis is often used to make generalisations across all available evidence at the global scale. But how can these global generalisations be used for evidence-based decision making at the local scale, if the global evidence is not perceived to be relevant to local decisions? We show how an interactive method of meta-analysis-dynamic meta-analysis-can be used to assess the local relevance of global evidence.
We developed Metadataset ( www.metadataset.com ) as a proof-of-concept for dynamic meta-analysis. Using Metadataset, we show how evidence can be filtered and weighted, and results can be recalculated, using dynamic methods of subgroup analysis, meta-regression, and recalibration. With an example from agroecology, we show how dynamic meta-analysis could lead to different conclusions for different subsets of the global evidence. Dynamic meta-analysis could also lead to a rebalancing of power and responsibility in evidence synthesis, since evidence users would be able to make decisions that are typically made by systematic reviewers-decisions about which studies to include (e.g. critical appraisal) and how to handle missing or poorly reported data (e.g. sensitivity analysis).
In this study, we show how dynamic meta-analysis can meet an important challenge in evidence-based decision making-the challenge of using global evidence for local decisions. We suggest that dynamic meta-analysis can be used for subject-wide evidence synthesis in several scientific disciplines, including agroecology and conservation biology. Future studies should develop standardised classification systems for the metadata that are used to filter and weight the evidence. Future studies should also develop standardised software packages, so that researchers can efficiently publish dynamic versions of their meta-analyses and keep them up-to-date as living systematic reviews. Metadataset is a proof-of-concept for this type of software, and it is open source. Future studies should improve the user experience, scale the software architecture, agree on standards for data and metadata storage and processing, and develop protocols for responsible evidence use.
荟萃分析常用于在全球范围内对所有可用证据进行概括。但是，如果全球证据被认为与当地决策无关，这些全球概括如何用于当地的循证决策？我们展示了一种交互式荟萃分析方法——动态荟萃分析——如何用于评估全球证据的局部相关性。
我们开发了 Metadataset（www.metadataset.com）作为动态荟萃分析的概念验证。使用 Metadataset，我们展示了如何使用动态亚组分析、荟萃回归和重新校准等方法来过滤和加权证据，并重新计算结果。通过农业生态学的一个例子，我们展示了动态荟萃分析如何针对全球证据的不同子集得出不同的结论。动态荟萃分析还可以导致证据综合中的权力和责任重新平衡，因为证据使用者将能够做出通常由系统评价者做出的决策，例如决定纳入哪些研究（例如，批判性评价）以及如何处理缺失或报告不佳的数据（例如，敏感性分析）。
在这项研究中，我们展示了动态荟萃分析如何应对循证决策中的一个重要挑战——使用全球证据进行本地决策的挑战。我们建议，动态荟萃分析可用于农业生态学和保护生物学等多个科学学科的全领域证据综合。未来的研究应开发用于过滤和加权证据的元数据的标准化分类系统。未来的研究还应开发标准化的软件包，以便研究人员能够高效地发布其荟萃分析的动态版本，并随着系统评价的更新而保持最新。Metadataset 是这种类型软件的概念验证，并且是开源的。未来的研究应改善用户体验、扩展软件架构、就数据和元数据存储和处理的标准达成一致，并制定负责任的证据使用协议。
