Multi-level statistical models in studies of periodontal diseases.
Periodontal data typically have a hierarchical structure, with sites grouped within individuals, and individuals grouped within communities. Also, the occasion may be regarded as another level since the acquired knowledge indicates that periodontal disease activity may vary over time. Conventional statistical tests are based on unilevel analysis of data. However, this approach to statistical analysis is often inconvenient in periodontal research because of the variation in the outcome variables between the various levels in the hierarchy. Lately there have been important developments in the statistical theory which have made available powerful statistical techniques for analyzing multilevel or hierarchical data. This report describes a new approach for analyzing periodontal data and uses an illustrative example to build a model which explains part of the variability in the response variable. The results from this analysis are then compared to results from an earlier report which uses unilevel methods and the findings discussed. The present multilevel approach has several advantages over unilevel methods, mainly due to its statistical validity and efficiency. Further, it permits the incorporation of explanatory variables measured at the site and the subject levels, and those which vary across the time points. Multilevel analyses have a promising potential and are expected to have a significant impact on periodontal research.
Albandar
J M
JM
Department of Periodontology, Dental Faculty, University of Oslo, Norway.
Goldstein
H
H
D000367:Age Factors; D016301:Alveolar Bone Loss / Q000453:epidemiology / Q000473:pathology; D000539:Alveolar Process / Q000473:pathology; D000704:Analysis of Variance; D003773:Dental Plaque / Q000453:epidemiology; D005260:Female; D006801:Humans; D008297:Male; D015233:Models, Statistical; D010510:Periodontal Diseases / Q000453:epidemiology*; D013997:Time Factors