title: Leveraging contact network structure in the design of cluster randomized trials.
abstract: BACKGROUND
  In settings like the Ebola epidemic, where proof-of-principle trials have provided evidence of efficacy but questions remain about the effectiveness of different possible modes of implementation, it may be useful to conduct 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, which can be obtained at study baseline-we propose a novel class of connectivity-informed cluster trial designs that aim both to improve public health impact (speed of epidemic control) and to preserve the ability to detect intervention effects.
  
  
  METHODS
  We several designs for 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. Our designs can accommodate connectivity based either on the total number of external connections at baseline or on connections only to areas yet to receive the intervention. We further consider a "holdback" version of the designs in which control clusters are held back from re-randomization for some time interval. We investigate the performance of these designs in terms of epidemic control outcomes (time to end of epidemic and cumulative incidence) and power to detect intervention effect, by simulating vaccination trials during an SEIR-type epidemic outbreak using a network-structured agent-based model. We compare results to those of a traditional Stepped Wedge trial.
  
  
  RESULTS
  In our simulation studies, connectivity-informed designs lead to a 20% reduction in cumulative incidence compared to comparable traditional study designs, but have little impact on epidemic length. Power to detect intervention effect is reduced in all connectivity-informed designs, but "holdback" versions provide power that is very close to that of a traditional Stepped Wedge approach.
  
  
  CONCLUSIONS
  Incorporating information about cluster connectivity in the 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 very modest cost in terms of power to detect effectiveness.
authors: 1 Department of Global Health and Population, Harvard T.H. Chan School of Public Health, Boston, MA, USA.
authors: Guy
authors: G
authors: Harling
authors: 2 Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA.|3 Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA.
authors: Rui
authors: R
authors: Wang
authors: 2 Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA.
authors: Jukka-Pekka
authors: JP
authors: Onnela
authors: 2 Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA.
authors: Victor
authors: V
authors: De Gruttola
mesh_terms: D003198:Computer Simulation; D004196:Disease Outbreaks; D046129:Ebola Vaccines / Q000627:therapeutic use; D019142:Hemorrhagic Fever, Ebola / Q000517:prevention & control; D006801:Humans; D011634:Public Health; D016032:Randomized Controlled Trials as Topic; D012107:Research Design; D013997:Time Factors
chemical_list: D046129:Ebola Vaccines
keywords: Ebola; Vaccine; cluster randomized trial; epidemic control; network; power