Utilizing Bayesian methods in clinical trials has become increasingly popular, as they can incorporate historical data and expert opinions into the design and allow for smaller sample sizes to reduce costs while providing reliable and robust statistical results. Sample size determination (SSD) is a key aspect of clinical trial design and various methods for Bayesian sample size determination are available. However, it is unclear how these methods are being used in practice. A systematic literature review was conducted to understand how sample sizes for Bayesian randomized clinical trials (RCTs) are determined and inform the design of future Bayesian trials. We searched five databases in May 2023, and updated in January 2025, including efficacy RCTs in humans which utilized a Bayesian framework for the primary data analysis, published in English, and enrolled participants between 2009 and 2024. The literature search produced 19,182 records, of which 105 studies were selected for data extraction. Results show that the most common method for SSD in Bayesian RCTs was a hybrid approach in which elements of Bayesian and frequentist theory are combined. Many RCTs did not provide a justification for SSD, while fully Bayesian methods were rarely used in practice, despite significant theoretical development. Our review also revealed a lack of standardized reporting, making it challenging to review the SSD. The CONSORT statement for reporting RCTs states that sample size calculations must be reported, which was poorly adhered to. Among RCTs that reported SSD, relevant information was frequently omitted from the reports and discussed in poorly structured supplementary materials. Thus, there is a critical need for greater transparency, standardization and translation of relevant methodology in Bayesian RCTs.

翻译：在临床试验中应用贝叶斯方法日益普及，因其能将历史数据和专家意见纳入试验设计，并允许使用更小的样本量以降低成本，同时提供可靠且稳健的统计结果。样本量确定是临床试验设计的关键环节，目前存在多种贝叶斯样本量确定方法。然而，这些方法在实际应用中的使用情况尚不明确。本研究通过系统性文献综述，旨在了解贝叶斯随机临床试验的样本量如何确定，并为未来贝叶斯试验的设计提供参考。我们于2023年5月检索了五个数据库，并于2025年1月进行了更新，纳入标准包括：采用贝叶斯框架进行主要数据分析的人体疗效随机临床试验、以英文发表、且在2009年至2024年间招募受试者。文献检索共获得19,182条记录，其中105项研究被选中进行数据提取。结果显示，贝叶斯随机临床试验中最常见的样本量确定方法是混合方法，即结合了贝叶斯理论和频率学派理论的要素。许多随机临床试验未提供样本量确定的依据，而完全贝叶斯方法尽管在理论上发展显著，但在实践中却很少使用。我们的综述还揭示了报告缺乏标准化的问题，这使得审查样本量确定过程变得困难。报告随机临床试验的CONSORT声明要求必须报告样本量计算，但这一规定遵守情况不佳。在报告了样本量确定的随机临床试验中，相关信息经常在报告中缺失，并在结构混乱的补充材料中进行讨论。因此，贝叶斯随机临床试验亟需更高的透明度、标准化以及相关方法的转化应用。