Analysis of cluster-randomized test-negative designs: cluster-level methods
| Autor: | Cameron P. Simmons, Nicholas P. Jewell, Katherine L. Anders, Suzanne M. Dufault, Zoe Cutcher |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2018 |
| Předmět: |
Statistics and Probability
Randomization Computer science Psychological intervention Inference Mosquito Vectors Biostatistics 01 natural sciences Dengue Random Allocation 010104 statistics & probability 03 medical and health sciences Aedes Intervention (counseling) Statistics Animals Humans 0101 mathematics Randomized Controlled Trials as Topic 030304 developmental biology Estimation 0303 health sciences Models Statistical Confounding General Medicine Odds ratio Articles Relative risk Statistics Probability and Uncertainty Corrigendum Wolbachia |
| Zdroj: | Biostatistics |
| Popis: | SUMMARY 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. We propose several methods for estimation and inference regarding the relative risk (RR). The inferential methods considered are based on the randomization distribution induced by permuting intervention assignment across two sets of randomly selected clusters. The motivating example is a current study of the efficacy of randomized releases of Wolbachia-infected Aedes aegypti mosquitoes to reduce the incidence of dengue in Yogyakarta City, Indonesia. Estimation and inference techniques are assessed through a simulation study. |
| Databáze: | OpenAIRE |
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