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

Bayesian methods for cluster randomized trials with continuous responses.

D J Spiegelhalter1

  • 1MRC Biostatistics Unit, Institute of Public Health, Robinson Way, Cambridge CB2 2SR, UK.

Statistics in Medicine
|February 17, 2001
PubMed
Summary
This summary is machine-generated.

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

Statistics. The future lies in uncertainty.

Science (New York, N.Y.)·2014
Same author

The power of the MicroMort.

BJOG : an international journal of obstetrics and gynaecology·2014
Same author

Using routine intelligence to target inspection of healthcare providers in England.

Quality & safety in health care·2009
Same author

How to interpret your dot: decoding the message of clinical performance indicators.

Journal of perinatology : official journal of the California Perinatal Association·2008
Same author

Flexible random-effects models using Bayesian semi-parametric models: applications to institutional comparisons.

Statistics in medicine·2006
Same author

Intracortically distributed neurovascular coupling relationships within and between human somatosensory cortices.

Cerebral cortex (New York, N.Y. : 1991)·2006
Same journal

Optimal Weighted Tests for Replication Studies and the 'Two-Trials Rule' With Multiple Hypotheses.

Statistics in medicine·2026
Same journal

Identifiable Copula-Double-Cox Models: A Fully Parametric Framework for Dependent Right-Censored Survival Data.

Statistics in medicine·2026
Same journal

Moving From Individualized Risk-Based Prevention to Benefit-Based Prevention: Estimating Individualized Life-Years Gained From Prevention Services as a Basis for Eligibility.

Statistics in medicine·2026
Same journal

A Mixture of Distributed Lag Non-Linear Models to Account for Spatially Heterogeneous Exposure-Lag-Response Associations.

Statistics in medicine·2026
Same journal

Practical Considerations for Gaussian Process Modeling for Causal Inference in Quasi-Experimental Studies With Panel Data.

Statistics in medicine·2026
Same journal

Covariate Adjustment for Wilcoxon Two Sample Statistic and Test.

Statistics in medicine·2026
See all related articles

Bayesian methods offer flexible analysis for cluster randomized trials, incorporating external data and relaxing standard assumptions. Careful prior specification is crucial, as demonstrated by their impact on study conclusions and power calculations.

Area of Science:

  • Biostatistics
  • Clinical Trials
  • Statistical Modeling

Background:

  • Cluster randomized trials (CRTs) often employ random-effects models.
  • Standard CRT models assume normality and constant variance, limiting flexibility.
  • Incorporating external evidence and relaxing assumptions can enhance CRT analyses.

Purpose of the Study:

  • To explore Bayesian methods for cluster randomized trials.
  • To investigate the impact of prior distribution choices on analysis outcomes.
  • To provide guidance on robust Bayesian strategies for CRTs.

Main Methods:

  • Application of Bayesian random-effects models.
  • Utilizing Markov chain Monte Carlo (MCMC) for model fitting.
  • Employing forward simulation for power calculations with parameter uncertainty.

Related Experiment Videos

Main Results:

  • Bayesian methods allow for external evidence integration and relaxed distributional assumptions.
  • Prior distribution choices significantly influenced study conclusions in an illustrative example.
  • Different prior specifications impact power calculations, highlighting the need for careful selection.

Conclusions:

  • Bayesian approaches offer a powerful, flexible framework for cluster randomized trials.
  • Robust analysis requires careful consideration and guidance on prior distribution specification.
  • Recommendations include using non-informative priors for within-cluster variance and specific priors for the intraclass correlation coefficient (ICC).