Flight Laura, Allison Annabel, Dimairo Munyaradzi, Lee Ellen, Mandefield Laura, Walters Stephen J
ScHARR, University of Sheffield, 30 Regent Street, Sheffield, S1 4DA, UK.
MRC Biostatistics Unit, Cambridge Institute of Public Health, Forvie Site, Robinson Way, Cambridge Biomedical Campus, Cambridge, CB2 0SR, UK.
BMC Med Res Methodol. 2016 Nov 29;16(1):165. doi: 10.1186/s12874-016-0249-5.
In an individually randomised controlled trial where the treatment is delivered by a health professional it seems likely that the effectiveness of the treatment, independent of any treatment effect, could depend on the skill, training or even enthusiasm of the health professional delivering it. This may then lead to a potential clustering of the outcomes for patients treated by the same health professional, but similar clustering may not occur in the control arm. Using four case studies, we aim to provide practical guidance and recommendations for the analysis of trials with some element of clustering in one arm.
Five approaches to the analysis of outcomes from an individually randomised controlled trial with clustering in one arm are identified in the literature. Some of these methods are applied to four case studies of completed randomised controlled trials with clustering in one arm with sample sizes ranging from 56 to 539. Results are obtained using the statistical packages R and Stata and summarised using a forest plot.
The intra-cluster correlation coefficient (ICC) for each of the case studies was small (<0.05) indicating little dependence on the outcomes related to cluster allocations. All models fitted produced similar results, including the simplest approach of ignoring clustering for the case studies considered.
A partially clustered approach, modelling the clustering in just one arm, most accurately represents the trial design and provides valid results. Modelling homogeneous variances between the clustered and unclustered arm is adequate in scenarios similar to the case studies considered. We recommend treating each participant in the unclustered arm as a single cluster. This approach is simple to implement in R and Stata and is recommended for the analysis of trials with clustering in one arm only. However, the case studies considered had small ICC values, limiting the generalisability of these results.
在一项由卫生专业人员提供治疗的个体随机对照试验中，治疗效果（独立于任何治疗效应）似乎可能取决于提供治疗的卫生专业人员的技能、培训甚至热情。这可能会导致由同一名卫生专业人员治疗的患者的结局出现潜在的聚集性，但对照组可能不会出现类似的聚集性。通过四个案例研究，我们旨在为分析单臂存在某种聚集性因素的试验提供实用指导和建议。
文献中确定了五种分析单臂存在聚集性的个体随机对照试验结局的方法。其中一些方法应用于四个已完成的单臂存在聚集性的随机对照试验案例研究，样本量从56到539不等。使用统计软件包R和Stata获得结果，并使用森林图进行总结。
每个案例研究的组内相关系数（ICC）都很小（<0.05），表明对与组分配相关的结局依赖性很小。所有拟合模型产生了相似的结果，包括对所考虑的案例研究忽略聚集性的最简单方法。
部分聚集性方法，即仅对单臂中的聚集性进行建模，最准确地代表了试验设计并提供了有效的结果。在与所考虑的案例研究类似的情况下，对聚集组和非聚集组之间的同质性方差进行建模就足够了。我们建议将非聚集组中的每个参与者视为一个单独的组。这种方法在R和Stata中易于实施，建议用于仅单臂存在聚集性的试验分析。然而，所考虑的案例研究的ICC值较小，限制了这些结果的普遍性。
