Bayesian hierarchical network autocorrelation models for estimating direct and indirect effects of peer hospitals on outcomes of hospitalized patients
Abstract When an hypothesized peer effect (also termed social influence or contagion) is believed to act between units (e.g., hospitals) above the level at which data is observed (e.g., patients), a network autocorrelation model may be embedded within a hierarchical data structure thereby formulatin...
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| Format: | Article |
| Language: | English |
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SpringerOpen
2024-06-01
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| Series: | Applied Network Science |
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| Online Access: | https://doi.org/10.1007/s41109-024-00627-1 |
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| author | Guanqing Chen A. James O’Malley |
| author_facet | Guanqing Chen A. James O’Malley |
| author_sort | Guanqing Chen |
| collection | DOAJ |
| description | Abstract When an hypothesized peer effect (also termed social influence or contagion) is believed to act between units (e.g., hospitals) above the level at which data is observed (e.g., patients), a network autocorrelation model may be embedded within a hierarchical data structure thereby formulating the peer effect as a dependency between latent variables. In such a situation, a patient’s own hospital can be thought of as a mediator between the effects of peer hospitals and their outcome. However, as in mediation analyses, there may be interest in allowing the effects of peer units to directly impact patients of other units. To accommodate these possibilities, we develop two hierarchical network autocorrelation models that allow for direct and indirect peer effects between hospitals when modeling individual outcomes of the patients cared for at the hospitals. A Bayesian approach is used for model estimation while a simulation study assesses the performance of the models and sensitivity of results to different prior distributions. We construct a United States New England region patient-sharing hospital network and apply newly developed Bayesian hierarchical models to study the diffusion of robotic surgery and hospital peer effects in patient outcomes using a cohort of United States Medicare beneficiaries in 2016 and 2017. The comparative fit of models to the data is assessed using Deviance information criteria tailored to hierarchical models that include peer effects as latent variables. |
| format | Article |
| id | doaj-art-8391aaebccca408fa2d462e2d716ef27 |
| institution | Kabale University |
| issn | 2364-8228 |
| language | English |
| publishDate | 2024-06-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | Applied Network Science |
| spelling | doaj-art-8391aaebccca408fa2d462e2d716ef272025-01-26T12:20:15ZengSpringerOpenApplied Network Science2364-82282024-06-019112510.1007/s41109-024-00627-1Bayesian hierarchical network autocorrelation models for estimating direct and indirect effects of peer hospitals on outcomes of hospitalized patientsGuanqing Chen0A. James O’Malley1Department of Anesthesia, Critical Care and Pain Medicine, Beth Israel Deaconess Medical Center, Harvard Medical SchoolDepartment of Biomedical Data Science , Geisel School of Medicine at DartmouthAbstract When an hypothesized peer effect (also termed social influence or contagion) is believed to act between units (e.g., hospitals) above the level at which data is observed (e.g., patients), a network autocorrelation model may be embedded within a hierarchical data structure thereby formulating the peer effect as a dependency between latent variables. In such a situation, a patient’s own hospital can be thought of as a mediator between the effects of peer hospitals and their outcome. However, as in mediation analyses, there may be interest in allowing the effects of peer units to directly impact patients of other units. To accommodate these possibilities, we develop two hierarchical network autocorrelation models that allow for direct and indirect peer effects between hospitals when modeling individual outcomes of the patients cared for at the hospitals. A Bayesian approach is used for model estimation while a simulation study assesses the performance of the models and sensitivity of results to different prior distributions. We construct a United States New England region patient-sharing hospital network and apply newly developed Bayesian hierarchical models to study the diffusion of robotic surgery and hospital peer effects in patient outcomes using a cohort of United States Medicare beneficiaries in 2016 and 2017. The comparative fit of models to the data is assessed using Deviance information criteria tailored to hierarchical models that include peer effects as latent variables.https://doi.org/10.1007/s41109-024-00627-1Bayesian inferenceDirect and indirect peer effectsDiffusion of robotic surgeryHierarchical network autocorrelation model |
| spellingShingle | Guanqing Chen A. James O’Malley Bayesian hierarchical network autocorrelation models for estimating direct and indirect effects of peer hospitals on outcomes of hospitalized patients Applied Network Science Bayesian inference Direct and indirect peer effects Diffusion of robotic surgery Hierarchical network autocorrelation model |
| title | Bayesian hierarchical network autocorrelation models for estimating direct and indirect effects of peer hospitals on outcomes of hospitalized patients |
| title_full | Bayesian hierarchical network autocorrelation models for estimating direct and indirect effects of peer hospitals on outcomes of hospitalized patients |
| title_fullStr | Bayesian hierarchical network autocorrelation models for estimating direct and indirect effects of peer hospitals on outcomes of hospitalized patients |
| title_full_unstemmed | Bayesian hierarchical network autocorrelation models for estimating direct and indirect effects of peer hospitals on outcomes of hospitalized patients |
| title_short | Bayesian hierarchical network autocorrelation models for estimating direct and indirect effects of peer hospitals on outcomes of hospitalized patients |
| title_sort | bayesian hierarchical network autocorrelation models for estimating direct and indirect effects of peer hospitals on outcomes of hospitalized patients |
| topic | Bayesian inference Direct and indirect peer effects Diffusion of robotic surgery Hierarchical network autocorrelation model |
| url | https://doi.org/10.1007/s41109-024-00627-1 |
| work_keys_str_mv | AT guanqingchen bayesianhierarchicalnetworkautocorrelationmodelsforestimatingdirectandindirecteffectsofpeerhospitalsonoutcomesofhospitalizedpatients AT ajamesomalley bayesianhierarchicalnetworkautocorrelationmodelsforestimatingdirectandindirecteffectsofpeerhospitalsonoutcomesofhospitalizedpatients |