M S Gilthorpe1, G S Griffiths, I H Maddick, A T Zamzuri. 1. Biostatistics Unit, Eastman Dental Institute for Oral Health Care Sciences, University College London, United Kingdom. m.gilthorpe@eastman.ucl.ac.uk
Abstract
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.
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.
Authors: Ian Needleman; Jean Suvan; Mark S Gilthorpe; Richard Tucker; Geoff St George; William Giannobile; Maurizio Tonetti; Martin Jarvis Journal: J Clin Periodontol Date: 2007-02-23 Impact factor: 8.728