Cluster-Randomised Test Negative Designs: Inference and Application to Vector Trials to Eliminate Dengue Department of Statistics

Nick Jewell, University of California, Berkeley School of Public Health, gives a talk for the departmental of Statistics on 28th May 2020. Abstract: The successful introduction of the intracellular bacterium Wolbachia into Aedes aegypti mosquitoes enables a practical approach for dengue prevention through release of Wolbachia-infected mosquitoes. Wolbachia reduces dengue virus replication in the mosquito and, once established in the mosquito population, it is possible that this will provide a long-term and sustainable approach to reducing or eliminating dengue transmission. A critical next step is to assess the efficacy of Wolbachia deployments in reducing dengue virus transmission in the field. I will describe and discuss the statistical design of a large-scale cluster randomised test-negative parallel arm study to measure the efficacy of such interventions. Comparison of permutation inferential approaches to model based methods will be described. Extensions to allow for individual covariates, and alternate designs such as the stepped wedge approach, will also be briefly introduced. There are also interesting questions regarding interrupted time-series methods associated with analysing pilot site data.