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Bayesian data analysis in observational comparative effectiveness research: rationale and examples.

Publication ,  Journal Article
Olson, WH; Crivera, C; Ma, Y-W; Panish, J; Mao, L; Lynch, SM
Published in: Journal of comparative effectiveness research
November 2013

Many comparative effectiveness research and patient-centered outcomes research studies will need to be observational for one or both of two reasons: first, randomized trials are expensive and time-consuming; and second, only observational studies can answer some research questions. It is generally recognized that there is a need to increase the scientific validity and efficiency of observational studies. Bayesian methods for the design and analysis of observational studies are scientifically valid and offer many advantages over frequentist methods, including, importantly, the ability to conduct comparative effectiveness research/patient-centered outcomes research more efficiently. Bayesian data analysis is being introduced into outcomes studies that we are conducting. Our purpose here is to describe our view of some of the advantages of Bayesian methods for observational studies and to illustrate both realized and potential advantages by describing studies we are conducting in which various Bayesian methods have been or could be implemented.

Duke Scholars

Published In

Journal of comparative effectiveness research

DOI

EISSN

2042-6313

ISSN

2042-6305

Publication Date

November 2013

Volume

2

Issue

6

Start / End Page

563 / 571

Related Subject Headings

  • Retrospective Studies
  • Research Design
  • Prospective Studies
  • Patient-Centered Care
  • Patient Outcome Assessment
  • Observational Studies as Topic
  • Humans
  • Delivery of Health Care
  • Comparative Effectiveness Research
  • Bayes Theorem
 

Citation

APA
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ICMJE
MLA
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Olson, W. H., Crivera, C., Ma, Y.-W., Panish, J., Mao, L., & Lynch, S. M. (2013). Bayesian data analysis in observational comparative effectiveness research: rationale and examples. Journal of Comparative Effectiveness Research, 2(6), 563–571. https://doi.org/10.2217/cer.13.73
Olson, William H., Concetta Crivera, Yi-Wen Ma, Jessica Panish, Lian Mao, and Scott M. Lynch. “Bayesian data analysis in observational comparative effectiveness research: rationale and examples.Journal of Comparative Effectiveness Research 2, no. 6 (November 2013): 563–71. https://doi.org/10.2217/cer.13.73.
Olson WH, Crivera C, Ma Y-W, Panish J, Mao L, Lynch SM. Bayesian data analysis in observational comparative effectiveness research: rationale and examples. Journal of comparative effectiveness research. 2013 Nov;2(6):563–71.
Olson, William H., et al. “Bayesian data analysis in observational comparative effectiveness research: rationale and examples.Journal of Comparative Effectiveness Research, vol. 2, no. 6, Nov. 2013, pp. 563–71. Epmc, doi:10.2217/cer.13.73.
Olson WH, Crivera C, Ma Y-W, Panish J, Mao L, Lynch SM. Bayesian data analysis in observational comparative effectiveness research: rationale and examples. Journal of comparative effectiveness research. 2013 Nov;2(6):563–571.
Journal cover image

Published In

Journal of comparative effectiveness research

DOI

EISSN

2042-6313

ISSN

2042-6305

Publication Date

November 2013

Volume

2

Issue

6

Start / End Page

563 / 571

Related Subject Headings

  • Retrospective Studies
  • Research Design
  • Prospective Studies
  • Patient-Centered Care
  • Patient Outcome Assessment
  • Observational Studies as Topic
  • Humans
  • Delivery of Health Care
  • Comparative Effectiveness Research
  • Bayes Theorem