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

Introduction to Bayesian modelling in dental research.

M S Gilthorpe1, I H Maddick, A Petrie

  • 1Biostatistics Unit, Eastman Dental Institute for Oral Health Care Sciences, University College London, UK. m.gilthorpe@eastman.ucl.ac.uk

Community Dental Health
|February 24, 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

Comparison of Histopathologic Features of Right and Left Sided Colon Cancer.

ANZ journal of surgery·2026
Same author

A complex systems approach to obesity: a transdisciplinary framework for action.

Perspectives in public health·2023
Same author

Expression of growth mediators in the gingival crevicular fluid of patients with aggressive periodontitis undergoing periodontal surgery.

Clinical oral investigations·2018
Same author

Clinical outcomes of a vitrified donor oocyte programme: A single UK centre experience.

European journal of obstetrics, gynecology, and reproductive biology·2018
Same author

Adjustment for time-invariant and time-varying confounders in 'unexplained residuals' models for longitudinal data within a causal framework and associated challenges.

Statistical methods in medical research·2018
Same author

Career satisfaction and work-life balance of specialist orthodontists within the UK/ROI.

British dental journal·2017
Same journal

School - based oral health promotion strategies: A global scoping review.

Community dental health·2026
Same journal

The primordial policy gap in dental caries control in Iran: From national DMFT success to the persistent challenge of the significant caries index (SIC): Policy brief.

Community dental health·2026
Same journal

Oral pain among young adolescents in Tanzania: A cross-sectional study.

Community dental health·2026
Same journal

Biosafety practices in Brazilian dentistry after the COVID-19 public health emergency: Persistence and discontinuation of protective measures.

Community dental health·2026
Same journal

Tobacco use and its impact on oral health among female adolescents in Riyadh, Saudi Arabia.

Community dental health·2026
Same journal

Associations between ethnicity, socioeconomic status and dental caries in children: A population-based record linkage longitudinal cohort study.

Community dental health·2026
See all related articles

Bayesian modeling offers a powerful approach to analyze dental research data by synthesizing diverse evidence. This statistical method enhances data interpretation and supports clinical decision-making.

Area of Science:

  • Statistics in Dentistry
  • Evidence Synthesis

Background:

  • Bayesian modeling provides a flexible framework for statistical analysis.
  • Its application in dental research is growing.

Purpose of the Study:

  • To elucidate the principles and applications of Bayesian modeling.
  • To demonstrate its utility in analyzing dental research data.

Main Methods:

  • Illustrative examples using hypothetical dental scenarios.
  • Synthesis of randomized controlled trial (RCT) results with prior evidence.
  • Introduction to empirical Bayesian modeling (meta-analysis).
  • Full Bayesian modeling for root canal treatment success.
  • Hierarchical Bayesian modeling for childhood caries surveys.

Related Experiment Videos

Main Results:

  • Bayesian methods facilitate enhanced interpretation of research findings.
  • Synthesis of information from multiple sources improves evidence quality.

Conclusions:

  • Bayesian modeling is accessible to clinical researchers.
  • It can augment clinical decision-making and guideline development.