ArticleJournal of the Royal Statistical Society. Series C, Applied statistics2021
Bayesian Criterion Based Variable Selection.
Article in Journal of the Royal Statistical Society. Series C, Applied statistics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT06268379 (Developing a Clinician-friendly Online Tool for Survival Prediction in Colon Cancer Patients), which is not on this map. Cited by 3 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Developing a Clinician-friendly Online Tool for Survival Prediction in Colon Cancer Patients: A Bayesian Model Averaging for Risk Factor Selection and Dynamic Nomogram
Who cites it
3 citing papers in PubMed.
- Developing a Clinician-Friendly Online Dynamic Nomogram for Survival Prediction in Colon Cancer Patients.Annals of gastroenterological surgery · 2026Article
- Prognostic factors and survival disparities in right-sided versus left-sided colon cancer.Scientific reports · 2024Article
- Bayesian variable selection and survival modeling: assessing the Most important comorbidities that impact lung and colorectal cancer survival in Spain.BMC medical research methodology · 2022Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
Bayesian approaches for criterion based selection include the marginal likelihood based highest posterior model (HPM) and the deviance information criterion (DIC). The DIC is popular in practice as it can often be estimated from sampling based methods with relative ease and DIC is readily available in various Bayesian software. We find that sensitivity of DIC based selection can be high, in the range of 90 - 100%. However, correct selection by DIC can be in the range of 0 - 2%. These performances persist consistently with increase in sample size. We establish that both marginal likelihood and DIC asymptotically disfavor under-fitted models, explaining the high sensitivities of both criteria. However, mis-selection probability of DIC remains bounded below by a positive constant in linear models with
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Registered trials
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