ABSTRACT
Previously, burst and linear theories for periodontal disease progression were proposed based on different but limited statistical methods of analysis. Multilevel modeling provides a new approach, yielding a more comprehensive model. Random coefficient models were used to analyze longitudinal periodontal data consisting of repeated measures (level 1), sites (level 2), teeth (level 3), and subjects (level 4). Large negative and highly significant correlations between random linear and quadratic time coefficients indicated that subjects and teeth with greater-than-average linear change experienced decelerated variation. Conversely, subjects and teeth with less-than-average linear change experienced accelerated variation. Change therefore exhibited a dynamic regression to the mean at the tooth and subject levels. Since no equilibrium was attained throughout the study, changes were cyclical. When considered as a multilevel system, the "linear" and "burst" theories of periodontal disease progression are a manifestation of the same phenomenon: Some sites improve while others progress, in a cyclical manner.
Subject(s)
Models, Biological , Periodontal Diseases/physiopathology , Adolescent , Adult , Confounding Factors, Epidemiologic , Disease Progression , Humans , Likelihood Functions , Male , Military Personnel , Periodontal Attachment Loss/pathology , Periodontal Index , Periodontal Pocket/pathology , Regression Analysis , Risk Factors , Surveys and QuestionnairesABSTRACT
OBJECTIVE: To introduce the concepts of random coefficient multilevel models through an application to periodontal research data. BASIC RESEARCH DESIGN: Multilevel models with random coefficients are illustrated using periodontal data that comprise four levels: repeated measurements at level-1, sites at level-2, teeth at level-3, and subjects at level-4. The study explores random coefficient models--where random variation occurs about explanatory variable coefficients. Outcomes considered are lifetime cumulative attachment loss and pocket probing depth. PARTICIPANTS: The study data were taken from a survey of periodontal disease involving 100 white male trainee engineers aged between 16 and 20 entering the apprentice training school at the Royal Air Force-Halton, UK. RESULTS: The application of multilevel modelling to longitudinal data provides a new way of exploring old problems. The multilevel random coefficient models provide an opportunity to examine the 'linear' and 'burst' theories of periodontal disease progression, leading to the postulation that both can be unified within the multilevel framework. CONCLUSIONS: The multilevel methodology illustrates how advances in the understanding of oral health can be achieved with the advent of new statistical methods
Subject(s)
Dental Research/methods , Dental Research/statistics & numerical data , Models, Statistical , Periodontal Diseases/epidemiology , Adolescent , Adult , Disease Progression , Humans , Linear Models , Longitudinal Studies , Male , Military Personnel , Periodontal Index , Periodontal Pocket/diagnosis , United Kingdom/epidemiologyABSTRACT
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.
Subject(s)
Bayes Theorem , Dental Research/methods , Models, Statistical , Confidence Intervals , Data Interpretation, Statistical , Humans , Likelihood Functions , Meta-Analysis as Topic , Reproducibility of Results , Statistical DistributionsABSTRACT
OBJECTIVE: To explain the concepts and application of multilevel modelling (MLM) and how it can be applied to the analysis of dental research data. BASIC DESIGN: Methodological in nature, this article introduces MLM through actual and hypothetical dental examples. SETTING: Examples of MLM in periodontal research illustrate cross-sectional and longitudinal analyses of hierarchical dental data. Multilevel multivariate modelling is illustrated by an example in orthodontic research. The potential implications of study design in the context MLM is extensively discussed, including study sample size and accounting for examiner variability. RESULTS: The fine detail and greater insight provided by MLM enables dental researchers to review and revise old hypotheses and to generate new ones that could not be addressed by single-level methods. CONCLUSIONS: MLM has the potential to increase our understanding of oral disease and health and offers the opportunity to rethink some aspects of dental research procedure.
Subject(s)
Dental Research/methods , Models, Statistical , Analysis of Variance , Cross-Sectional Studies , Data Collection , Data Interpretation, Statistical , Humans , Longitudinal Studies , Multivariate Analysis , Observer Variation , Orthodontics/methods , Periodontics/methods , Reproducibility of Results , Research Design , Sample SizeABSTRACT
OBJECTIVE: To explain the theory of multilevel modelling and demonstrate its application in the analysis of dental research data. BASIC RESEARCH DESIGN: Multilevel modelling was introduced using dental data comprising four levels: repeated measurements at level-1, sites at level-2, teeth at level-3, and subjects at level-4. Variance components models (which have no explanatory variables) were evaluated for all outcome measures. Explanatory variables were added to the models with outcomes for both lifetime cumulative attachment loss and pocket probing depth. Salient features of the multilevel models were discussed. PARTICIPANTS: Research data were obtained from a longitudinal survey of periodontal disease conducted on 100 white male trainee engineers aged between 16 and 20 years entering the apprentice training school at Royal Air Force Halton, England. RESULTS: The statistical methods revealed that periodontal measures demonstrate considerable variation at all levels of the multilevel structure. Models for lifetime cumulative attachment loss and pocket probing depth illustrated that risk factors operated at more than one level. Supragingival calculus was a risk factor at the subject-level (subjects experiencing more sites with the condition had greater attachment loss and greater pocketing) whilst there was apparently a protective effect occurring at the site (sites with the condition had less attachment loss and less pocketing). CONCLUSIONS: This study demonstrates that multilevel modelling is a more powerful research tool than single-level techniques for the analysis of hierarchical dental data. Researchers using these techniques are well equipped to analyse complex hierarchical data structures, such as those often found within dentistry.
