Abstract：
Single-case designs (SCDs) are used to evaluate the effects of interventions on individual participants. By repeatedly measuring participants under different conditions, SCD studies focus on individual effects rather than on group summaries. The main limitation of SCDs remains its generalisability to wider populations, reducing the relevance of their findings for practice and policy making. With this limitation in mind, methodological developments for synthesising SCD data from different studies that investigate the same research question have intensified in the past decades (e.g. multilevel modelling). However, these techniques are restricted to comparing two interventions at a time and can only incorporate evidence from studies that directly compare the two treatments of interest. These limitations could be addressed by using network meta-analysis that incorporates both direct and indirect evidence to simultaneously compare multiple interventions. Despite its potential, network meta-analytical techniques have yet to be applied to SCD data. Thus, in this paper, we argue that network meta-analysis can be a valuable tool to synthesise SCD data. We demonstrate the use of network meta-analysis in SCD data using a real dataset, and we conclude by reflecting on the challenges that SCD researchers might face when applying network meta-analysis methods to their data.
摘要：
单病例设计(SCD)用于评估干预措施对个体参与者的影响。通过在不同条件下重复测量参与者，SCD研究侧重于个体影响，而不是群体总结。SCD的主要局限性仍然是其对更广泛人群的普遍性，降低他们的发现与实践和政策制定的相关性。考虑到这个限制，在过去的几十年中，从调查同一研究问题的不同研究中合成SCD数据的方法学发展（例如，多层次建模）得到了加强。然而,这些技术仅限于一次比较两种干预措施，并且只能纳入直接比较两种感兴趣治疗方法的研究证据。这些限制可以通过使用结合直接和间接证据的网络荟萃分析来解决，以同时比较多种干预措施。尽管有潜力,网络元分析技术尚未应用于SCD数据。因此，在本文中，我们认为，网络荟萃分析可能是一个有价值的工具，以综合SCD数据。我们使用真实的数据集演示了网络荟萃分析在SCD数据中的使用，我们通过反思SCD研究人员在将网络荟萃分析方法应用于其数据时可能面临的挑战来得出结论。
