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| Format: | Artículo Open Access |
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Wiley
2024
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| Online Access: | https://onlinelibrary.wiley.com/doi/10.1002/sim.10113 |
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| _version_ | 1867014785701249024 |
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| author | Erica M. Porter Christopher T. Franck Stephen Adams |
| author_facet | Erica M. Porter Christopher T. Franck Stephen Adams Erica M. Porter Christopher T. Franck Stephen Adams |
| collection | Wiley Open Access |
| contents | Flexible cost‐penalized Bayesian model selection: Developing inclusion paths with an application to diagnosis of heart disease Erica M. Porter Christopher T. Franck Stephen Adams Statistics in Medicine We propose a Bayesian model selection approach that allows medical practitioners to select among predictor variables while taking their respective costs into account. Medical procedures almost always incur costs in time and/or money. These costs might exceed their usefulness for modeling the outcome of interest. We develop Bayesian model selection that uses flexible model priors to penalize costly predictors a priori and select a subset of predictors useful relative to their costs. Our approach (i) gives the practitioner control over the magnitude of cost penalization, (ii) enables the prior to scale well with sample size, and (iii) enables the creation of our proposed inclusion path visualization, which can be used to make decisions about individual candidate predictors using both probabilistic and visual tools. We demonstrate the effectiveness of our inclusion path approach and the importance of being able to adjust the magnitude of the prior's cost penalization through a dataset pertaining to heart disease diagnosis in patients at the Cleveland Clinic Foundation, where several candidate predictors with various costs were recorded for patients, and through simulated data. 10.1002/sim.10113 http://creativecommons.org/licenses/by/4.0/ |
| doi_str_mv | 10.1002/sim.10113 |
| format | Artículo Open Access |
| id | wiley_oa_10_1002_sim_10113 |
| institution | Wiley Open Access |
| license_str_mv | http://creativecommons.org/licenses/by/4.0/ |
| publishDate | 2024 |
| publisher | Wiley |
| record_format | wiley_oa |
| spellingShingle | Flexible cost‐penalized Bayesian model selection: Developing inclusion paths with an application to diagnosis of heart disease Erica M. Porter Christopher T. Franck Stephen Adams Statistics in Medicine Flexible cost‐penalized Bayesian model selection: Developing inclusion paths with an application to diagnosis of heart disease Erica M. Porter Christopher T. Franck Stephen Adams Statistics in Medicine We propose a Bayesian model selection approach that allows medical practitioners to select among predictor variables while taking their respective costs into account. Medical procedures almost always incur costs in time and/or money. These costs might exceed their usefulness for modeling the outcome of interest. We develop Bayesian model selection that uses flexible model priors to penalize costly predictors a priori and select a subset of predictors useful relative to their costs. Our approach (i) gives the practitioner control over the magnitude of cost penalization, (ii) enables the prior to scale well with sample size, and (iii) enables the creation of our proposed inclusion path visualization, which can be used to make decisions about individual candidate predictors using both probabilistic and visual tools. We demonstrate the effectiveness of our inclusion path approach and the importance of being able to adjust the magnitude of the prior's cost penalization through a dataset pertaining to heart disease diagnosis in patients at the Cleveland Clinic Foundation, where several candidate predictors with various costs were recorded for patients, and through simulated data. 10.1002/sim.10113 http://creativecommons.org/licenses/by/4.0/ |
| title | Flexible cost‐penalized Bayesian model selection: Developing inclusion paths with an application to diagnosis of heart disease |
| topic | Statistics in Medicine |
| url | https://onlinelibrary.wiley.com/doi/10.1002/sim.10113 |