Bayesian model averaging: improved variable selection for matched case-control studies
Advanced Search
Select up to three search categories and corresponding keywords using the fields to the right. Refer to the Help section for more detailed instructions.

Search our Collections & Repository

For very narrow results

When looking for a specific result

Best used for discovery & interchangable words

Recommended to be used in conjunction with other fields

Dates

to

Document Data
Library
People
Clear All
Clear All

For additional assistance using the Custom Query please check out our Help Page

CDC STACKS serves as an archival repository of CDC-published products including scientific findings, journal articles, guidelines, recommendations, or other public health information authored or co-authored by CDC or funded partners. As a repository, CDC STACKS retains documents in their original published format to ensure public access to scientific information.
i

Bayesian model averaging: improved variable selection for matched case-control studies

Filetype[PDF-531.33 KB]


English
Details Supporting Files You May Also Like

Details:

  • Alternative Title:
    Epidemiol Biostat Public Health
  • Personal Author:
  • 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:
  • 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
  • Volume:
    16
  • Issue:
    2
  • Collection(s):
  • Main Document Checksum:
  • Download URL:
  • File Type:

You May Also Like

Checkout today's featured content at stacks.cdc.gov