Interim data monitoring in cluster randomised trials: Practical issues and a case study.
Interim data monitoring in cluster randomised trials: Practical issues and a case study.
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DOI：
10.1177/17407745211024751
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发表时间：
2021-10
期刊：
影响因子：
--
通讯作者：
Middleton L
中科院分区：
文献类型：
--
作者：
Hemming K;Martin J;Gallos I;Coomarasamy A;Middleton L
对于个体随机试验的中期监测，有大量的指南。虽然存在关于如何将这些方法扩展到群集随机试验的方法学文献，但实际实施的指导很少。聚类试验有许多特点，这使得它们的监测需求不同。我们概述了方法和集群试验的临时监测的实际挑战，并将这些考虑因素的案例研究。E-MOTIVE研究是一项80组随机试验，采用一系列干预措施治疗产后出血。拟定的数据监测计划包括（1）监测样本量假设，（2）监测选择偏倚的证据，（3）主要结局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，既考虑了统计精度，又考虑了有时间招募更多的聚类。监测选择偏倚包括比较研究组之间招募的个体水平特征和数量，以确定任何随机化后受试者识别偏倚。使用Haybittle-Peto方法对99.9%置信区间的结局进行中期分析，应减轻对I类错误膨胀的任何担忧。试验的实用性意味着对依从性的监测是不相关的，因为它是一个过程评价的组成部分。聚类试验的中期分析与监测个体随机试验有许多重要差异。在集群试验中，往往会有一个更大的需要监测滋扰参数，但往往会有相当大的不确定性，在他们的估计。这意味着样本量重新估计的效用可能是值得怀疑的，特别是当对计划的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，中期监测有可能识别选择偏倚，特别是在随机化后识别或招募的试验中。最后，聚类试验的实用性可能意味着，允许基于统计检验对结局进行中期监测的方法的实用性，或监测对研究干预措施的依从性，不太相关。我们的目的是促进规划未来的集群随机试验，并促进讨论和辩论，以改善这些研究的监测。
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. 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. 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.
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