Salanti Georgia, Nikolakopoulou Adriani, Sutton Alex J, Reichenbach Stephan, Trelle Sven, Naci Huseyin, Egger Matthias
Institute of Social and Preventive Medicine (ISPM), University of Bern, Bern, Switzerland.
Department of Health Sciences, College of Medicine, Biological Sciences and Psychology, University of Leicester, Leicester, UK.
Trials. 2018 Jul 11;19(1):365. doi: 10.1186/s13063-018-2740-2.
The important role of network meta-analysis of randomized clinical trials in health technology assessment and guideline development is increasingly recognized. This approach has the potential to obtain conclusive results earlier than with new standalone trials or conventional, pairwise meta-analyses.
Network meta-analyses can also be used to plan future trials. We introduce a four-step framework that aims to identify the optimal design for a new trial that will update the existing evidence while minimizing the required sample size. The new trial designed within this framework does not need to include all competing interventions and comparisons of interest and can contribute direct and indirect evidence to the updated network meta-analysis. We present the method by virtually planning a new trial to compare biologics in rheumatoid arthritis and a new trial to compare two drugs for relapsing-remitting multiple sclerosis.
A trial design based on updating the evidence from a network meta-analysis of relevant previous trials may require a considerably smaller sample size to reach the same conclusion compared with a trial designed and analyzed in isolation. Challenges of the approach include the complexity of the methodology and the need for a coherent network meta-analysis of previous trials with little heterogeneity.
When used judiciously, conditional trial design could significantly reduce the required resources for a new study and prevent experimentation with an unnecessarily large number of participants.
随机临床试验的网络荟萃分析在卫生技术评估和指南制定中的重要作用日益得到认可。与新的独立试验或传统的成对荟萃分析相比，这种方法有可能更早地获得确定性结果。
网络荟萃分析也可用于规划未来的试验。我们介绍了一个四步框架，旨在为新试验确定最佳设计，该设计将更新现有证据，同时使所需样本量最小化。在此框架内设计的新试验无需纳入所有感兴趣的竞争干预措施和比较，可为更新后的网络荟萃分析提供直接和间接证据。我们通过虚拟规划一项比较类风湿关节炎生物制剂的新试验和一项比较复发缓解型多发性硬化症两种药物的新试验来展示该方法。
与单独设计和分析的试验相比，基于更新相关既往试验网络荟萃分析证据的试验设计可能需要小得多的样本量就能得出相同结论。该方法的挑战包括方法的复杂性以及需要对既往试验进行异质性较小的连贯网络荟萃分析。
如果明智地使用，条件性试验设计可显著减少新研究所需的资源，并避免让不必要的大量参与者参与试验。
