Medical Statistics, Faculty of Health: Medicine, Dentistry and Human Sciences, University of Plymouth, Room N15, ITTC Building 1, Plymouth Science Park, Plymouth, Devon, PL6 8BX, UK.
NIHR ARC South West Peninsula (PenARC), College of Medicine and Health, University of Exeter, Exeter, Devon, UK.
Syst Rev. 2021 Mar 31;10(1):91. doi: 10.1186/s13643-021-01637-1.
In a cluster randomised controlled trial (CRCT), randomisation units are "clusters" such as schools or GP practices. This has methodological implications for study design and statistical analysis, since clustering often leads to correlation between observations which, if not accounted for, can lead to spurious conclusions of efficacy/effectiveness. Bayesian methodology offers a flexible, intuitive framework to deal with such issues, but its use within CRCT design and analysis appears limited. This review aims to explore and quantify the use of Bayesian methodology in the design and analysis of CRCTs, and appraise the quality of reporting against CONSORT guidelines.
We sought to identify all reported/published CRCTs that incorporated Bayesian methodology and papers reporting development of new Bayesian methodology in this context, without restriction on publication date or location. We searched Medline and Embase and the Cochrane Central Register of Controlled Trials (CENTRAL). Reporting quality metrics according to the CONSORT extension for CRCTs were collected, as well as demographic data, type and nature of Bayesian methodology used, journal endorsement of CONSORT guidelines, and statistician involvement.
Twenty-seven publications were included, six from an additional hand search. Eleven (40.7%) were reports of CRCT results: seven (25.9%) were primary results papers and four (14.8%) reported secondary results. Thirteen papers (48.1%) reported Bayesian methodological developments, the remaining three (11.1%) compared different methods. Four (57.1%) of the primary results papers described the method of sample size calculation; none clearly accounted for clustering. Six (85.7%) clearly accounted for clustering in the analysis. All results papers reported use of Bayesian methods in the analysis but none in the design or sample size calculation.
The popularity of the CRCT design has increased rapidly in the last twenty years but this has not been mirrored by an uptake of Bayesian methodology in this context. Of studies using Bayesian methodology, there were some differences in reporting quality compared to CRCTs in general, but this study provided insufficient data to draw firm conclusions. There is an opportunity to further develop Bayesian methodology for the design and analysis of CRCTs in order to expand the accessibility, availability, and, ultimately, use of this approach.
在一项整群随机对照试验（CRCT）中，随机单位为“群组”，如学校或全科医生诊所。这对研究设计和统计分析具有方法学意义，因为聚类通常会导致观察结果之间的相关性，如果不加以考虑，可能会导致关于疗效/有效性的虚假结论。贝叶斯方法为处理此类问题提供了一个灵活、直观的框架，但它在 CRCT 设计和分析中的应用似乎有限。本综述旨在探讨和量化贝叶斯方法在 CRCT 设计和分析中的应用，并根据 CONSORT 指南评估报告的质量。
我们试图确定所有纳入贝叶斯方法的报告/已发表的 CRCT 以及在此背景下报告新贝叶斯方法开发的论文，不限制发表日期或地点。我们搜索了 Medline 和 Embase 以及 Cochrane 中央对照试验注册中心（CENTRAL）。收集了根据 CONSORT 扩展版用于 CRCT 的报告质量指标，以及人口统计学数据、使用的贝叶斯方法的类型和性质、期刊对 CONSORT 指南的认可以及统计员的参与情况。
共纳入 27 篇文献，另外通过手工搜索增加了 6 篇。其中 11 篇（40.7%）为 CRCT 结果报告：7 篇（25.9%）为主要结果论文，4 篇（14.8%）为次要结果论文。13 篇论文（48.1%）报告了贝叶斯方法的发展，其余 3 篇（11.1%）比较了不同的方法。4 篇（57.1%）主要结果论文描述了样本量计算方法；没有明确考虑聚类。6 篇（85.7%）在分析中明确考虑了聚类。所有结果论文都报告了在分析中使用了贝叶斯方法，但在设计或样本量计算中没有使用。
在过去的二十年中，CRCT 设计的普及速度迅速加快，但在这种情况下，贝叶斯方法的应用并没有与之相匹配。使用贝叶斯方法的研究在报告质量上与一般的 CRCT 相比存在一些差异，但本研究提供的数据不足以得出明确的结论。有机会进一步发展贝叶斯方法用于 CRCT 的设计和分析，以扩大这种方法的可及性、可用性，并最终加以应用。
