Leveraging Contact Network Information in Clustered Randomized Studies of Contagion Processes.
| dc.rights.license | open | en_US |
| dc.contributor.author | WANG, Maxwell H | |
| dc.contributor.author | STAPLES, Patrick | |
| hal.structure.identifier | Statistics In System biology and Translational Medicine [SISTM] | |
| hal.structure.identifier | Bordeaux population health [BPH] | |
| dc.contributor.author | PRAGUE, Melanie | |
| dc.contributor.author | GOYAL, Ravi | |
| dc.contributor.author | DEGRUTTOLA, Victor | |
| dc.contributor.author | ONNELA, Jukka-Pekka | |
| dc.date.accessioned | 2023-10-04T14:25:09Z | |
| dc.date.available | 2023-10-04T14:25:09Z | |
| dc.date.issued | 2023 | |
| dc.identifier.issn | 2767-3324 | en_US |
| dc.identifier.uri | https://oskar-bordeaux.fr/handle/20.500.12278/184329 | |
| dc.description.abstractEn | In a randomized study, leveraging covariates related to the outcome (e.g. disease status) may produce less variable estimates of the effect of exposure. For contagion processes operating on a contact network, transmission can only occur through ties that connect affected and unaffected individuals; the outcome of such a process is known to depend intimately on the structure of the network. In this paper, we investigate the use of contact network features as efficiency covariates in exposure effect estimation. Using augmented generalized estimating equations (GEE), we estimate how gains in efficiency depend on the network structure and spread of the contagious agent or behavior. We apply this approach to simulated randomized trials using a stochastic compartmental contagion model on a collection of model-based contact networks and compare the bias, power, and variance of the estimated exposure effects using an assortment of network covariate adjustment strategies. We also demonstrate the use of network-augmented GEEs on a clustered randomized trial evaluating the effects of wastewater monitoring on COVID-19 cases in residential buildings at the the University of California San Diego. | |
| dc.language.iso | EN | en_US |
| dc.subject.en | Network | |
| dc.subject.en | Contagion | |
| dc.subject.en | Statistics | |
| dc.title.en | Leveraging Contact Network Information in Clustered Randomized Studies of Contagion Processes. | |
| dc.title.alternative | Obs Stud | en_US |
| dc.type | Article de revue | en_US |
| dc.identifier.doi | 10.1353/obs.2023.0021 | en_US |
| dc.subject.hal | Sciences du Vivant [q-bio]/Santé publique et épidémiologie | en_US |
| dc.identifier.pubmed | 37325081 | en_US |
| bordeaux.journal | Observational Studies | en_US |
| bordeaux.page | 157-175 | en_US |
| bordeaux.volume | 9 | en_US |
| bordeaux.hal.laboratories | Bordeaux Population Health Research Center (BPH) - UMR 1219 | en_US |
| bordeaux.issue | 2 | en_US |
| bordeaux.institution | Université de Bordeaux | en_US |
| bordeaux.institution | INSERM | en_US |
| bordeaux.institution | INRIA | en_US |
| bordeaux.team | SISTM | en_US |
| bordeaux.peerReviewed | oui | en_US |
| bordeaux.inpress | non | en_US |
| bordeaux.identifier.funderID | National Institutes of Health | en_US |
| bordeaux.import.source | pubmed | |
| hal.popular | non | en_US |
| hal.audience | Internationale | en_US |
| hal.export | false | |
| workflow.import.source | pubmed | |
| dc.rights.cc | Pas de Licence CC | en_US |
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