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| Autores principales: | , , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| Materias: | |
| Acceso en línea: | https://arxiv.org/abs/2405.05909 |
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| _version_ | 1866915705148932096 |
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| author | Si, Yajuan Tran, Toan Gabry, Jonah Morris, Mitzi Gelman, Andrew |
| author_facet | Si, Yajuan Tran, Toan Gabry, Jonah Morris, Mitzi Gelman, Andrew |
| contents | We present a novel Bayesian workflow for multilevel regression and poststratification (MRP), introducing extensions to time-varying data and granular geography and publicly available open-source computation tools, facilitating broad research adoption and reproducibility. In the absence of comprehensive or random testing throughout the COVID-19 pandemic, we have developed a proxy method for synthetic random sampling to estimate community-level viral incidence, based on viral RNA testing of asymptomatic patients who present for elective procedures within a hospital system. The approach collects routine testing data on SARS-CoV-2 exposure among outpatients and performs statistical adjustments of sample representation using MRP, a procedure that adjusts for nonrepresentativeness of the sample and yields stable small group estimates. We illustrate the MRP interface with an application to track community-level COVID-19 viral transmission in the state of Michigan. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_05909 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Multilevel Regression and Poststratification Interface: An Application to Track Community-level COVID-19 Viral Transmission Si, Yajuan Tran, Toan Gabry, Jonah Morris, Mitzi Gelman, Andrew Applications We present a novel Bayesian workflow for multilevel regression and poststratification (MRP), introducing extensions to time-varying data and granular geography and publicly available open-source computation tools, facilitating broad research adoption and reproducibility. In the absence of comprehensive or random testing throughout the COVID-19 pandemic, we have developed a proxy method for synthetic random sampling to estimate community-level viral incidence, based on viral RNA testing of asymptomatic patients who present for elective procedures within a hospital system. The approach collects routine testing data on SARS-CoV-2 exposure among outpatients and performs statistical adjustments of sample representation using MRP, a procedure that adjusts for nonrepresentativeness of the sample and yields stable small group estimates. We illustrate the MRP interface with an application to track community-level COVID-19 viral transmission in the state of Michigan. |
| title | Multilevel Regression and Poststratification Interface: An Application to Track Community-level COVID-19 Viral Transmission |
| topic | Applications |
| url | https://arxiv.org/abs/2405.05909 |