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An application of multilevel modelling to longitudinal periodontal research data.

Mark S. Gilthorpe, G. S. Griffiths, Ian H. Maddick, A T Zamzuri

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Abstract

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

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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

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Available abstract

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

Key concepts: Multilevel model, Random effects model, Multilevel modelling, Medicine, Generalized linear mixed model, Mixed model, Dental research, Statistics

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