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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
Objective:
To explain the concepts and application of Bayesian modelling and how it can be applied to the analysis of dental research data.
Basic Design:
Methodological in nature, this article introduces Bayesian modelling through hypothetical dental examples.
Setting:
The synthesis of RCT results with previous evidence, including expert opinion, is used to illustrate full Bayesian modelling. Meta-analysis, in the form of empirical Bayesian modelling, is introduced. An example of full Bayesian modelling is described for the synthesis of evidence from several studies that investigate the success of root canal treatment. Hierarchical (Bayesian) modelling is demonstrated for a survey of childhood caries, where surface data is nested within subjects.
Results:
Bayesian methods enhance interpretation of research evidence through the synthesis of information from multiple sources.
Conclusions:
Bayesian modelling is now readily accessible to clinical researchers and is able to augment the application of clinical decision making in the development of guidelines and clinical practice.