Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Experiment Videos

Prime time for Bayes

J B Kadane1

  • 1Department of Statistics, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213, USA.

Controlled Clinical Trials
|October 1, 1995
PubMed
Summary
This summary is machine-generated.

Bayesian statistics, using explicit prior distributions and likelihoods, offers advantages for clinical trials. Advances in this methodology position it as a primary inferential tool for medical research.

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Bayesian demography: projecting the Iraqi Kurdish population, 1977-1990.

Journal of the American Statistical Association·2002
Same author

Approaches for optimal sequential decision analysis in clinical trials.

Biometrics·1998
Same author

Bayesian statistical methods in public health and medicine.

Annual review of public health·1995
Same author

Adjusting the 1980 census of population and housing.

Journal of the American Statistical Association·1989
Same author

Progress toward a more ethical method for clinical trials.

The Journal of medicine and philosophy·1986
Same author

Using administrative lists to estimate census omissions.

Journal of official statistics·1986

Area of Science:

  • Statistics
  • Biostatistics
  • Clinical Trials Methodology

Background:

  • Bayesian statistics is a framework for statistical inference.
  • A common criticism of Bayesian statistics involves the subjective nature of prior distributions, likelihoods, and loss functions.
  • Despite this criticism, explicit statement of these components is a key strength.

Purpose of the Study:

  • To review the principles of Bayesian statistics.
  • To highlight the advantages of explicitly stated subjective components in Bayesian analysis.
  • To advocate for the adoption of Bayesian methods in clinical trials.

Main Methods:

  • Literature review of Bayesian statistical principles.
  • Argumentative analysis of the advantages of subjective components in Bayesian inference.

Related Experiment Videos

  • Discussion of technological advancements enabling Bayesian applications.

    • Main Results:

    • Bayesian statistics requires explicit definition of prior distributions, likelihood, and loss functions.
    • This explicitness is presented as a significant advantage rather than a disadvantage.
    • Current advancements in Bayesian technology support its use in complex applications.

    Conclusions:

    • The explicit nature of Bayesian components enhances transparency and reproducibility.
    • Bayesian statistical technology has matured, making it suitable for widespread use.
    • Bayesian inference is proposed as the leading inferential tool for clinical trials.