Background

There is an abundance of guidance for the interim monitoring of individually randomised trials. While methodological literature exists on how to extend these methods to cluster randomised trials, there is little guidance on practical implementation. Cluster trials have many features which make their monitoring needs different. We outline the methodological and practical challenges of interim monitoring of cluster trials; and apply these considerations to a case study.

Case study

The E-MOTIVE study is an 80-cluster randomised trial of a bundle of interventions to treat postpartum haemorrhage. The proposed data monitoring plan includes (1) monitor sample size assumptions, (2) monitor for evidence of selection bias, and (3) an interim assessment of the primary outcome, as well as monitoring data completeness. The timing of the sample size monitoring is chosen with both consideration of statistical precision and to allow time to recruit more clusters. Monitoring for selection bias involves comparing individual-level characteristics and numbers recruited between study arms to identify any post-randomisation participant identification bias. An interim analysis of outcomes presented with 99.9% confidence intervals using the Haybittle–Peto approach should mitigate any concern regarding the inflation of type-I error. The pragmatic nature of the trial means monitoring for adherence is not relevant, as it is built into a process evaluation.

Conclusions

The interim analyses of cluster trials have a number of important differences to monitoring individually randomised trials. In cluster trials, there will often be a greater need to monitor nuisance parameters, yet there will often be considerable uncertainty in their estimation. This means the utility of sample size re-estimation can be questionable particularly when there are practical or funding difficulties associated with making any changes to planned sample sizes. Perhaps most importantly interim monitoring has the potential to identify selection bias, particularly in trials with post-randomisation identification or recruitment. Finally, the pragmatic nature of cluster trials might mean that the utility of methods to allow for interim monitoring of outcomes based on statistical testing, or monitoring for adherence to study interventions, are less relevant. Our intention is to facilitate the planning of future cluster randomised trials and to promote discussion and debate to improve monitoring of these studies.

中文翻译：

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整群随机试验中的中期数据监测：实际问题和案例研究

背景

对于单独随机试验的中期监测有大量指导。尽管存在关于如何将这些方法扩展到整群随机试验的方法学文献，但对实际实施的指导很少。集群试验有很多特点，这使得它们的监控需求有所不同。我们概述了集群试验临时监测的方法和实际挑战；并将这些考虑因素应用到案例研究中。

案例分析

E-MOTIVE 研究是一项 80 组随机试验，涉及一系列治疗产后出血的干预措施。拟议的数据监测计划包括（1）监测样本量假设，（2）监测选择偏差的证据，以及（3）主要结果的中期评估以及监测数据完整性。选择样本量监测的时间时既要考虑统计精度，又要留出时间招募更多的聚类。选择偏差的监测包括比较个体水平的特征和研究组之间招募的人数，以识别任何随机化后的参与者识别偏差。使用 Haybitttle-Peto 方法对结果进行的 99.9% 置信区间的中期分析应该可以减轻对 I 类错误膨胀的担忧。试验的务实性质意味着对依从性的监测并不重要，因为它是过程评估的一部分。

结论

整群试验的中期分析与监测单独的随机试验有许多重要的区别。在集群试验中，通常更需要监测干扰参数，但其估计往往存在相当大的不确定性。这意味着样本量重新估计的效用可能会受到质疑，特别是当对计划样本量进行任何更改存在实际或资金困难时。也许最重要的是，中期监测有可能发现选择偏差，特别是在随机化后识别或招募的试验中。最后，集群试验的实用性可能意味着基于统计测试对结果进行临时监测或监测研究干预措施依从性的方法的实用性不太重要。我们的目的是促进未来整群随机试验的规划，并促进讨论和辩论，以改善对这些研究的监测。