Human and mosquito movement can lead to underestimation of the intervention effect in trials of vector interventions; as such, the protective efficacy of Wolbachia is likely even higher than reported in the primary trial results.
Introgression of wMel into A. aegypti populations is effective in reducing the focal transmission of dengue virus, leading to reduced case incidence.
We propose the log-contrast estimator that can eliminate bias caused by differential healthcare-seeking behavior in cluster-randomized test-negative designs and demonstrate how to improve precision by adjusting for covariates.
Introgression of wMel into A. aegypti populations was effective in reducing the incidence of symptomatic dengue and resulted in fewer hospitalizations for dengue among the participants.
In cluster randomized trials (CRTs), the outcome of interest is often a count at the cluster level. This occurs, for example, in evaluating an intervention with the outcome being the number of infections of a disease such as HIV or dengue or the number of hospitalizations in the cluster. Standard practice analyzes these counts through cluster outcome rates using an appropriate denominator (eg, population size). However, such denominators are sometimes unknown, particularly when the counts depend on a passive community surveillance system. We consider direct comparison of the counts without knowledge of denominators, relying on randomization to balance denominators. We also focus on permutation tests to allow for small numbers of randomized clusters. However, such approaches are subject to bias when there is differential ascertainment of counts across arms, a situation that may occur in CRTs that cannot implement blinded interventions. We suggest the use of negative control counts as a method to remove, or reduce, this bias, discussing the key properties necessary for an effective negative control. A current example of such a design is the recent extension of test-negative designs to CRTs testing community-level interventions. Via simulation, we compare the performance of new and standard estimators based on CRTs with negative controls to approaches that only use the original counts.When there is no differential ascertainment by intervention arm, the count-only approaches perform comparably to those using debiasing negative controls. However, under even modest differential ascertainment, the count-only estimators are no longer reliable.
Vector-borne diseases are a growing threat to global health. I help develop statistical methods to rigorously evaluate the impact of novel preventive interventions.
Proposing and comparing methods of estimation for cluster-randomized test-negative design trials.
Intervention trials of vector control methods often require community level randomization with appropriate inferential methods. For many interventions, the possibility of confounding due to the effects of health-care seeking behavior on disease ascertainment remains a concern. The test-negative design, a variant of the case-control method, was introduced to mitigate this issue in the assessment of the efficacy of influenza vaccination (measured at an individual level) on influenza infection. Here, we introduce a cluster-randomized test-negative design that includes randomization of the intervention at a group level.