Both individually and cluster randomized study designs have been used for vaccine trials to assess the effects of vaccine on reducing the risk of disease or infection. The choice between individually and cluster randomized designs is often driven by the target estimand of interest (eg, direct versus total), statistical power, and, importantly, logistic feasibility. To combat emerging infectious disease threats, especially when the number of events from one single trial may not be adequate to obtain vaccine effect estimates with a desired level of precision, it may be necessary to combine information across multiple trials. In this article, we propose a model formulation to estimate the direct, indirect, total, and overall vaccine effects combining data from trials with two types of study designs: individual-randomization and cluster-randomization, based on a Cox proportional hazards model, where the hazard of infection depends on both vaccine status of the individual as well as the vaccine status of the other individuals in the same cluster. We illustrate the use of the proposed model and assess the potential efficiency gain from combining data from multiple trials, compared to using data from each individual trial alone, through two simulation studies, one of which is designed based on a cholera vaccine trial previously carried out in Matlab, Bangladesh.

中文翻译：

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通过结合个体随机试验和整群随机试验的数据来估计直接和间接疫苗接种效果的方法

单独和整群随机研究设计已用于疫苗试验，以评估疫苗对降低疾病或感染风险的效果。单独随机设计和集群随机设计之间的选择通常由感兴趣的目标估计（例如，直接与总体）、统计功效以及重要的是逻辑可行性驱动。为了应对新出现的传染病威胁，特别是当一项试验的事件数量可能不足以获得所需精度水平的疫苗效果估计时，可能有必要结合多个试验的信息。在本文中，我们提出了一种模型公式来估计直接、间接、总体和总体疫苗效果，将试验数据与两种类型的研究设计相结合：个体随机化和集群随机化，基于 Cox 比例风险模型，其中感染的危险取决于个体的疫苗状态以及同一群体中其他个体的疫苗状态。我们通过两项模拟研究说明了所提出的模型的用途，并评估了与单独使用每个单独试验的数据相比，组合多个试验的数据所带来的潜在效率增益，其中一项是根据之前进行的霍乱疫苗试验设计的在孟加拉国的 Matlab 中。