This study presents a hybrid (Bayesian‐frequentist) approach to sample size re‐estimation (SSRE) for cluster randomised trials with continuous outcome data, allowing for uncertainty in the intra‐cluster correlation (ICC). In the hybrid framework, pre‐trial knowledge about the ICC is captured by placing a Truncated Normal prior on it, which is then updated at an interim analysis using the study data, and used in expected power control. On average, both the hybrid and frequentist approaches mitigate against the implications of misspecifying the ICC at the trial's design stage. In addition, both frameworks lead to SSRE designs with approximate control of the type I error‐rate at the desired level. It is clearly demonstrated how the hybrid approach is able to reduce the high variability in the re‐estimated sample size observed within the frequentist framework, based on the informativeness of the prior. However, misspecification of a highly informative prior can cause significant power loss. In conclusion, a hybrid approach could offer advantages to cluster randomised trials using SSRE. Specifically, when there is available data or expert opinion to help guide the choice of prior for the ICC, the hybrid approach can reduce the variance of the re‐estimated required sample size compared to a frequentist approach. As SSRE is unlikely to be employed when there is substantial amounts of such data available (ie, when a constructed prior is highly informative), the greatest utility of a hybrid approach to SSRE likely lies when there is low‐quality evidence available to guide the choice of prior.

中文翻译：

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具有连续结果的整群随机试验中样本量重新估计的混合方法


本研究提出了一种混合（贝叶斯频率）方法，用于具有连续结果数据的聚类随机试验的样本量重新估计（SSRE），允许聚类内相关性（ICC）的不确定性。在混合框架中，通过在其上放置截断正态来捕获有关 ICC 的审前知识，然后使用研究数据在中期分析中进行更新，并用于预期功率控制。平均而言，混合方法和频率方法都减轻了在试验设计阶段错误指定 ICC 的影响。此外，这两个框架都导致 SSRE 设计将 I 类错误率大致控制在所需的水平。它清楚地证明了混合方法如何能够根据先验的信息量减少在频率论框架内观察到的重新估计样本量的高变异性。然而，错误指定信息丰富的先验可能会导致严重的功率损失。总之，混合方法可以为使用 SSRE 的聚类随机试验提供优势。具体来说，当有可用数据或专家意见来帮助指导 ICC 先验选择时，与频率论方法相比，混合方法可以减少重新估计所需样本量的方差。由于当有大量此类数据可用时（即，当构造的先验信息丰富时）不太可能采用 SSRE，因此 SSRE 混合方法的最大效用可能在于当存在低质量证据可用于指导优先的选择。