Multimorbidity analysis with low condition counts: A robust Bayesian approach for small but important subgroups

Poster from the publicationRomero Moreno G., Restocchi V., Fleuriot J., Anand A., Mercer S., Guthrie B., “Multimorbidity analysis withlow condition counts:A robust Bayesian approach for small but important subgroups”, eBioMedicine, Volume102, 2024, 105081, https://www.sciencedirect.com/science/article/pii/S2352396424001166

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Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2024-09-09
DOI
https://doi.org/10.5281/zenodo.13691896
Primary Topic
Chronic Disease Management Strategies
Type
article
Field-Weighted Citation Impact
0.00
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article

Multimorbidity analysis with low condition counts: A robust Bayesian approach for small but important subgroups

Zenodo (CERN European Organization for Nuclear Research)
Chronic Disease Management Strategies
article

Multimorbidity analysis with low condition counts: A robust Bayesian approach for small but important subgroups

article en

Abstract

Poster from the publicationRomero Moreno G., Restocchi V., Fleuriot J., Anand A., Mercer S., Guthrie B., “Multimorbidity analysis withlow condition counts:A robust Bayesian approach for small but important subgroups”, eBioMedicine, Volume102, 2024, 105081, https://www.sciencedirect.com/science/article/pii/S2352396424001166

Zenodo (CERN European Organization for Nuclear Research)
University of Edinburgh (GB)
Openalex Percentile: Top 59%
Chronic Disease Management Strategies
DOIOpen Access PDFOpenAlex
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Multimorbidity analysis with low condition counts: A robust Bayesian approach for small but important subgroups · Zenodo (CERN European Organization for Nuclear Research) (2024) | TGRS Research Map | TGRS