Details:

Alternative Title:
Epidemiol Biostat Public Health
Personal Author:
Mu, Yi ; See, Isaac ; Edwards, Jonathan R.
Mu, Yi ; See, Isaac ; Edwards, Jonathan R. Less -
Description:
Background:

The 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.

Methods:

This 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 Staphylococcus aureus infections after hospital discharge 2011–2013.

Results:

Bayesian 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.

Conclusion:

Bayesian 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.


Subjects:
[+]
Article Bayesian Model Averaging Gibbs Variable Selection Matched Case Control Model Selection Zellner’s G-prior
Source:
Epidemiol Biostat Public Health. 16(2)
Pubmed ID:
31772926
Pubmed Central ID:
PMC6879006
Document Type:
Journal Article
Funding:
CC999999/ImCDC/Intramural CDC HHS/United States
Collection(s):
CDC Public Access
Main Document Checksum:
[+]
urn:sha256:7258fb58910b63e83f5bb0407aecf91f427070aec2599f71e78896ffdeb47c70
Download URL:
https://stacks.cdc.gov/view/cdc/83420/cdc_83420_DS1.pdf
File Type:

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