ArticleEpidemiology, biostatistics and public health2019
Bayesian model averaging: improved variable selection for matched case-control studies.
Article in Epidemiology, biostatistics and public health, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
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- A Web-Based Dynamic Nomogram to Predict the Risk of Methicillin-Resistant Staphylococcal Infection in Patients with Pneumonia.Diagnostics (Basel, Switzerland) · 2024Article
- The association of upper airway anatomy with cognitive test performance: the Multi-Ethnic Study of Atherosclerosis.BMC neurology · 2023Article
- Metacognition, public health compliance, and vaccination willingness.Proceedings of the National Academy of Sciences of the United States of America · 2023Article
- Factors Associated With Telemedicine Use Among Patients With Rheumatic and Musculoskeletal Disease: Secondary Analysis of Data From a German Nationwide Survey.Journal of medical Internet research · 2023Article
- Factors Associated With Telemedicine Use Among German General Practitioners and Rheumatologists: Secondary Analysis of Data From a Nationwide Survey.Journal of medical Internet research · 2022Article
- Bayesian Model Averaging to Account for Model Uncertainty in Estimates of a Vaccine's Effectiveness.Clinical epidemiology · 2022Article
- Sustained attention induces altered effective connectivity of the ascending thalamo-cortical relay in obsessive-compulsive disorder.Frontiers in psychiatry · 2022Article
- Nicotine dependence and quitting stages of smokers in Nepal: A community based cross-sectional study.PloS one · 2022Article
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Authors and funding
3 authors.
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Abstract
backgroundThe problem of variable selection for risk factor modeling is an ongoing challenge in statistical practice. Classical methods that select one subset of exploratory risk factors dominate the medical research field. However, this approach has been criticized for not taking into account the uncertainty of the model selection process itself. This limitation can be addressed by a Bayesian model averaging approach: instead of focusing on a single model and a few factors, Bayesian model averaging considers all the models with non-negligible probabilities to make inference.
methodsThis paper reports on a simulation study designed to emulate a matched case-control study and compares classical versus Bayesian model averaging selection methods. We used Matthews's correlation coefficient to measure the quality of binary classifications. Both classical and Bayesian model averaging were also applied and compared for the analysis of a matched case-control study of patients with methicillin-resistant
resultsBayesian model averaging outperformed the classical approach with much lower false positive rates and higher Matthew's correlation scores. Bayesian model averaging also produced more reliable and robust effect estimates.
conclusionBayesian model averaging is a conceptually simple, unified approach that produces robust results. It can be used to replace controversial P-values for case-control study in medical research.
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