Background

Infectious disease interventions are increasingly tested using cluster-randomized trials (CRTs). These trial settings tend to involve a set of sampling units, such as villages, whose geographic arrangement may present a contamination risk in treatment exposure. The most widely used approach for reducing contamination in these settings is the so-called fried-egg design, which excludes the outer portion of all available clusters from the primary trial analysis. However, the fried-egg design ignores potential intra-cluster spatial heterogeneity and makes the outcome measure inherently less precise. Whereas the fried-egg design may be appropriate in specific settings, alternative methods to optimize the design of CRTs in other settings are lacking.

Methods

We present a novel approach for CRT design that either fully includes or fully excludes available clusters in a defined study region, recognizing the potential for intra-cluster spatial heterogeneity. The approach includes an algorithm that allows investigators to identify the maximum number of clusters that could be included for a defined study region and maintain randomness in both the selection of included clusters and the allocation of clusters to either the treatment group or control group. The approach was applied to the design of a CRT testing the effectiveness of malaria vector-control interventions in southern Malawi.

Conclusions

Those planning CRTs to evaluate interventions should consider the approach presented here during trial design. The approach provides a novel framework for reducing the risk of contamination among the CRT randomization units in settings where investigators determine the reduction of contamination risk as a high priority and where intra-cluster spatial heterogeneity is likely. By maintaining randomness in the allocation of clusters to either the treatment group or control group, the approach also permits a randomization-valid test of the primary trial hypothesis.

中文翻译：

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减少簇随机传染病干预试验中的污染风险

背景

传染病干预措施越来越多地使用聚类随机试验（CRT）进行测试。这些试验环境往往涉及一组采样单位，例如村庄，其地理布置可能会在治疗暴露中带来污染风险。在这些环境中减少污染的最广泛使用的方法是所谓的煎蛋设计，该设计从主要试验分析中排除了所有可用簇的外部。但是，煎蛋设计忽略了潜在的群集内部空间异质性，并使得结果度量本质上不那么精确。尽管煎蛋设计可能适合于特定的环境，但缺乏在其他环境中优化CRT设计的替代方法。

方法

我们提出了一种用于CRT设计的新颖方法，该方法完全或完全排除了定义的研究区域中的可用群集，从而认识到群集内空间异质性的潜力。该方法包括一种算法，该算法允许研究人员确定可用于定义研究区域的最大簇数，并在选择所包括的簇以及将簇分配给治疗组或对照组时均保持随机性。该方法被应用于CRT的设计，以测试马拉维南部疟疾媒介控制干预措施的有效性。

结论

那些计划CRT来评估干预措施的人应该考虑在试验设计过程中介绍的方法。该方法提供了一种新颖的框架，用于在以下情况下降低CRT随机单位之间的污染风险：研究人员将降低污染风险作为首要任务，并且可能发生集群内部空间异质性。通过维持将聚类分配给治疗组或对照组的随机性，该方法还允许对主要试验假设进行随机有效检验。