Correspondence: Guy Harling, Department of Global Health & Population, Harvard T.H. Chan School of Public Health, 1639 Tremont Street, Boston, MA 02120. Acknowledgements: The authors thank participants at the 2015 Ebola Modeling Workshop at Georgia Tech, at Healthmap and in the Onnela and De Gruttola lab groups for comments on presentations of these ideas, and Laura Balzer for her several helpful comments on a draft of this paper. This research was supported by the National Institutes of Health [grant numbers R37 AI51164, R01 AI24643].


Background: In settings like the Ebola epidemic, where proof-of-principle trials have succeeded but questions remain about the effectiveness of different possible modes of implementation, it may be useful to develop trials that not only generate information about intervention effects but also themselves provide public health benefit. Cluster randomized trials are of particular value for infectious disease prevention research by virtue of their ability to capture both direct and indirect effects of intervention; the latter of which depends heavily on the nature of contact networks within and across clusters. By leveraging information about these networks – in particular the degree of connection across randomized units – we propose a novel class of connectivity-informed cluster trial designs that aim both to improve public health impact (speed of control l epidemics) while preserving the ability to detect intervention effects.

Methods: We consider cluster randomized trials with staggered enrollment, in each of which the order of enrollment is based on the total number of ties (contacts) from individuals within a cluster to individuals in other clusters. These designs can accommodate connectivity based either on the total number of inter-cluster connections at baseline or on connections only to untreated clusters, and include options analogous both to traditional Parallel and Stepped Wedge designs. We further allow for control clusters to be “held-back” from re-randomization for some period. We investigate the performance of these designs in terms of epidemic control (time to end of epidemic and cumulative incidence) and power to detect vaccine effect by simulating vaccination trials during an SEIR-type epidemic outbreak using a network-structured agent-based model.

Results: In our simulations, connectivity-informed designs lead to lower peak infectiousness than comparable traditional study designs and a 20% reduction in cumulative incidence, but have little impact on epidemic length. Power to detect differences in incidence across clusters is reduced in all connectivity-informed designs. However the inclusion of even a brief “holdback” restores most of the power lost in comparison to a traditional Stepped Wedge approach.

Conclusions: Incorporating information about cluster connectivity in design of cluster randomized trials can increase their public health impact, especially in acute outbreak settings. Using this information helps control outbreaks – by minimizing the number of cross-cluster infections – with modest cost in power to detect an effective intervention.



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