{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"omics_type":["Unknown"],"volume":["17(2)"],"submitter":["Wang B"],"pubmed_abstract":["In 2019, the World Health Organization identified dengue as one of the top 10 global health threats. For the control of dengue, the Applying Wolbachia to Eliminate Dengue (AWED) study group conducted a cluster-randomized trial in Yogyakarta, Indonesia, and used a novel design, called the cluster-randomized test-negative design (CR-TND). This design can yield valid statistical inference with data collected by a passive surveillance system and thus has the advantage of cost-efficiency compared to traditional cluster-randomized trials. We investigate the statistical assumptions and properties of CR-TND under a randomization inference framework, which is known to be robust for small-sample problems. We find that, when the differential healthcare-seeking behavior comparing intervention and control varies across clusters (in contrast to the setting of Dufault and Jewell (Stat. Med.39 (2020a) 1429-1439) where the differential healthcare-seeking behavior is constant across clusters), current analysis methods for CR-TND can be biased and have inflated type I error. We propose the log-contrast estimator that can eliminate such bias and improve precision by adjusting for covariates. Furthermore, we extend our methods to handle partial intervention compliance and a stepped-wedge design, both of which appear frequently in cluster-randomized trials. Finally, we demonstrate our results by simulation studies and reanalysis of the AWED study."],"journal":["The annals of applied statistics"],"pagination":["1592-1614"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC12374749"],"repository":["biostudies-literature"],"pubmed_title":["RANDOMIZATION INFERENCE FOR CLUSTER-RANDOMIZED TEST-NEGATIVE DESIGNS WITH APPLICATION TO DENGUE STUDIES: UNBIASED ESTIMATION, PARTIAL COMPLIANCE, AND STEPPED-WEDGE DESIGN."],"pmcid":["PMC12374749"],"pubmed_authors":["Dufault SM","Wang B","Jewell NP","Small DS"],"additional_accession":[]},"is_claimable":false,"name":"RANDOMIZATION INFERENCE FOR CLUSTER-RANDOMIZED TEST-NEGATIVE DESIGNS WITH APPLICATION TO DENGUE STUDIES: UNBIASED ESTIMATION, PARTIAL COMPLIANCE, AND STEPPED-WEDGE DESIGN.","description":"In 2019, the World Health Organization identified dengue as one of the top 10 global health threats. For the control of dengue, the Applying Wolbachia to Eliminate Dengue (AWED) study group conducted a cluster-randomized trial in Yogyakarta, Indonesia, and used a novel design, called the cluster-randomized test-negative design (CR-TND). This design can yield valid statistical inference with data collected by a passive surveillance system and thus has the advantage of cost-efficiency compared to traditional cluster-randomized trials. We investigate the statistical assumptions and properties of CR-TND under a randomization inference framework, which is known to be robust for small-sample problems. We find that, when the differential healthcare-seeking behavior comparing intervention and control varies across clusters (in contrast to the setting of Dufault and Jewell (Stat. Med.39 (2020a) 1429-1439) where the differential healthcare-seeking behavior is constant across clusters), current analysis methods for CR-TND can be biased and have inflated type I error. We propose the log-contrast estimator that can eliminate such bias and improve precision by adjusting for covariates. Furthermore, we extend our methods to handle partial intervention compliance and a stepped-wedge design, both of which appear frequently in cluster-randomized trials. Finally, we demonstrate our results by simulation studies and reanalysis of the AWED study.","dates":{"release":"2023-01-01T00:00:00Z","publication":"2023 Jun","modification":"2026-04-08T10:03:11.282Z","creation":"2026-04-08T01:11:09.022Z"},"accession":"S-EPMC12374749","cross_references":{"pubmed":["40855892"],"doi":["10.1214/22-aoas1684"]}}