Incorporating Contact Network Structure in Cluster Randomized Trials
Incorporating Contact Network Structure in Cluster Randomized Trials
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DOI：
10.1038/srep17581
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发表时间：
2015-12-03
影响因子：
4.6
通讯作者：
Onnela, Jukka-Pekka
中科院分区：
文献类型：
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作者：
Staples, Patrick C.;Ogburn, Elizabeth L.;Onnela, Jukka-Pekka
只要有可能，通过随机将一些人分配到治疗组和另一些人作为对照，并比较两组之间的结果，来调查新治疗的有效性。通常，当治疗的目标是减缓一种传染病时，每一支治疗臂都会分配一群人。簇内和簇间相互作用的结构可能会降低试验的威力，即正确检测实际治疗效果的概率。我们通过模拟一个集群集合上的传染过程，研究了权力、集群内结构、通过集群间混合产生的交叉污染和传染性之间的关系。我们证明，与基于模拟的方法相比，当前基于公式的功率计算对于低水平的星系间混合可能是保守的，但如果不能考虑中等或高水平的功率计算，可能会导致研究严重不足。权力还依赖于集群内的网络结构，以实现某些类型的传染性传播。通过高度联系的个人机会性传播的感染会出现不可预测的感染爆发，使人们更难区分随机变异和真正的治疗效果。我们的方法可以在进行使用网络信息评估功率的试验之前使用，并演示了经验数据如何告知集群间混合的程度。
Whenever possible, the efficacy of a new treatment is investigated by randomly assigning some individuals to a treatment and others to control, and comparing the outcomes between the two groups. Often, when the treatment aims to slow an infectious disease, clusters of individuals are assigned to each treatment arm. The structure of interactions within and between clusters can reduce the power of the trial, i.e. the probability of correctly detecting a real treatment effect. We investigate the relationships among power, within-cluster structure, cross-contamination via between-cluster mixing, and infectivity by simulating an infectious process on a collection of clusters. We demonstrate that compared to simulation-based methods, current formula-based power calculations may be conservative for low levels of between-cluster mixing, but failing to account for moderate or high amounts can result in severely underpowered studies. Power also depends on within-cluster network structure for certain kinds of infectious spreading. Infections that spread opportunistically through highly connected individuals have unpredictable infectious breakouts, making it harder to distinguish between random variation and real treatment effects. Our approach can be used before conducting a trial to assess power using network information, and we demonstrate how empirical data can inform the extent of between-cluster mixing.