elimination; cluster randomization; malaria; Stepped-wedge design
Multerer Lea, Smith Thomas, Chitnis Nakul (2019), Modeling the impact of sterile males on an Aedes aegypti population with optimal control, in Mathematical Biosciences
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CI Jarvis, L Multerer, D Lewis, F Binka, WJ Edmunds, N Alexander, TA Smith (2019), Spatial Effects of Permethrin-Impregnated Bed Nets on Child Mortality: 26 Years on, a Spatial Reanalysis of a Cluster Randomized Trial., in The American journal of tropical medicine and hygiene
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Tiono Alfred B, Ouédraogo Alphonse, Ouattara Daouda, Bougouma Edith C, Coulibaly Sam, Diarra Amidou, Faragher Brian, Guelbeogo Moussa W, Grisales Nelson, Ouédraogo Issa N, Ouédraogo Zininwindé Amidou, Pinder Margaret, Sanon Souleymane, Smith Tom, Vanobberghen Fiona, Sagnon N'Fale, Ranson Hilary, Lindsay Steve W (2018), Efficacy of Olyset Duo, a bednet containing pyriproxyfen and permethrin, versus a permethrin-only net against clinical malaria in an area with highly pyrethroid-resistant vectors in rural Burkina Faso: a cluster-randomised controlled trial, in The Lancet
, 392(10147), 569-580.
Halloran M. Elizabeth, Auranen Kari, Baird Sarah, Basta Nicole E., Bellan Steven E., Brookmeyer Ron, Cooper Ben S., DeGruttola Victor, Hughes James P., Lessler Justin, Lofgren Eric T., Longini Ira M., Onnela Jukka-Pekka, Özler Berk, Seage George R., Smith Thomas A., Vespignani Alessandro, Vynnycky Emilia, Lipsitch Marc (2017), Simulations for designing and interpreting intervention trials in infectious diseases, in BMC Medicine
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Cluster-randomized trials (CRTs) are used to evaluate health interventions that have effects at the community level. In such trials, the effects of the intervention may spill-over into adjacent areas of control clusters leading to contamination effects. Trials of interventions against infectious diseases increasingly aim to evaluate the potential to interrupt pathogen transmission at maximum scale-up. To achieve this, there is a need for stepped-wedge designs. In stepped-wedge cluster-randomised trials (SWCRTs), assignment of clusters to the intervention, and hence the contamination effects, are time dependent. Established statistical methods seek to minimize effects of contamination, and hence neither measure it, nor exploit the information that it provides. Rather than aiming to avoid contamination, we propose that contamination effects may provide valuable evidence about the intervention effectiveness. Contamination, assessed via sub-cluster spatial variation in outcomes and patterns of outcomes across cluster boundaries, should be both measured as a trial outcome and used to make inferences about the properties of the intervention when deployed in non-trial settings. However, while intervention assignment in such trials is randomly assigned, spatial configuration is not. Measures of the contamination effect therefore depend jointly on factors over which trial participants are randomized and factors where randomization plays no role, and this raises novel issues in causal inference. This project will develop the required statistical methods for allowing for contamination in trial design, for analyzing the extent of contamination and using this for causal inference, and generalization of intervention impacts to non-trial settings. This methodological development will entail deriving:1.Point and interval estimates of the extent of contamination in CRTs and SWCRTs based on measuring gradients in outcomes across boundaries between trial arms. Some trials have assessed these gradients but without accounting for the correlation structure of the data. There is a need for practicable analytical approaches that account for this correlation structure in both estimating the gradient, and in estimating power to test if the gradient is non-zero. 2.Algorithm(s) for optimising cluster size and randomization strategies for CRTs and SWCRTs in the presence of contamination. These will be based both on considerations of statistical power of outcome measures, including measures of contamination effect, and in the case of SWCRTs, of how the evidence for causality is influenced by stratification in the trial randomization. The novel analytical approaches will be applied to two field trials of vector control interventions against malaria, (i) the Solar Power for Malaria Control (SolarMal) trial of the use of odour-baited mosquito traps to eliminate Plasmodium falciparum malaria from Rusinga Island, Lake Victoria, Kenya, and (ii) the AvecNet trial which is assessing the impact of treating mosquito nets with pyriproxyfen (an insect juvenile hormone mimic) in an area of rural Burkina Faso. Methods will be developed for generalising these trial results to settings where the interventions are implemented with different patterns of transmission and of intervention coverage from those in the trials. In particular, the estimates of contamination effects in SWCRTs will be used in parameterising mathematical models for predicting the impact of these interventions on unmeasured outcomes (such as mortality) over longer timelines than those in the trials.