INSERM, UMR1153, Epidemiology and Biostatistics Sorbonne Paris Cité Center (CRESS), METHODS team, Paris, France; French Cochrane Center, Paris, France; Direction de la recherche Clinique, Hôpital Foch, Suresnes, France.
INSERM, UMR1153, Epidemiology and Biostatistics Sorbonne Paris Cité Center (CRESS), METHODS team, Paris, France; French Cochrane Center, Paris, France; Centre d'Epidémiologie Clinique, AP-HP (Assistance Publique des Hôpitaux de Paris), Hôpital Hôtel Dieu, Paris, France; Paris Descartes University, Sorbonne Paris Cité, Faculté de Médecine, Paris, France.
J Clin Epidemiol. 2020 Jul;123:143-152. doi: 10.1016/j.jclinepi.2020.01.023. Epub 2020 Mar 4.
To become user driven and more useful for decision-making, the current evidence synthesis ecosystem requires significant changes (Paper 1. Future of evidence ecosystem series). Reviewers have access to new sources of data (clinical trial registries, protocols, and clinical study reports from regulatory agencies or pharmaceutical companies) for more information on randomized control trials. With all these newly available data, the management of multiple and scattered trial reports is even more challenging. New types of data are also becoming available: individual patient data and routinely collected data. With the increasing number of diverse sources to be searched and the amount of data to be extracted, the process needs to be rethought. New approaches and tools, such as automation technologies and crowdsourcing, should help accelerate the process. The implementation of these new approaches and methods requires a substantial rethinking and redesign of the current evidence synthesis ecosystem. The concept of a "living" evidence synthesis enterprise, with living systematic review and living network meta-analysis, has recently emerged. Such an evidence synthesis ecosystem implies conceptualizing evidence synthesis as a continuous process built around a clinical question of interest and no longer as a small team independently answering a specific clinical question at a single point in time.
为了使用户能够驱动并更有助于决策,当前的证据综合生态系统需要进行重大变革(文献 1. 证据生态系统的未来系列)。评审人员可以访问新的数据源(临床试验注册处、方案和来自监管机构或制药公司的临床研究报告),以获取更多关于随机对照试验的信息。有了所有这些新的可用数据,管理多个分散的试验报告更加具有挑战性。新类型的数据也在不断出现:个体患者数据和常规收集的数据。随着需要搜索的不同来源数量的增加和需要提取的数据量的增加,这个过程需要重新思考。自动化技术和众包等新方法和工具应该有助于加速这个过程。这些新方法和手段的实施需要对当前的证据综合生态系统进行大量的重新思考和重新设计。最近出现了一种“活”的证据综合企业的概念,即活的系统评价和活的网络荟萃分析。这种证据综合生态系统意味着将证据综合概念化为一个围绕感兴趣的临床问题构建的持续过程,而不再是一个小团队在某个特定时间独立回答一个特定的临床问题。