In order to account for the hierarchical structure of periodontal disease measurements, i.e. sites measurements clustered within teeth and then teeth clustered within individuals, analysis using a multilevel approach is required. Objective: The aim of this paper was to investigate baseline factors which may predict non-surgical periodontal treatment response using multilevel multiple regression. Methods: 32 non-smoking, chronic periodontitis patients participated in a single-blinded, randomised controlled clinical trial of non-surgical periodontal treatment protocols. 6-month reduction in probing pocket depth (PPD) of 4680 sites distributed on 806 teeth in these 32 patients was analysed by a multilevel approach. A 3-level model was considered: site at level-1, tooth at level-2 and subject at level-3. 12 independent predictor variables, 8 on subject-level, 1 on tooth-level and 3 on site-level were included in the multilevel multiple regression. The analysis was performed using the software MLwiN version 1.1. Results: Significant variations existed at all three levels of the multilevel structure (p<0.001). Multilevel multiple regression showed that 3 predictors on subject-level, 1 on tooth-level and 2 on site-level were significantly associated with 6-month reduction in PPD (p<0.001). Female subjects, subjects with higher % bleeding sites at baseline, subjects with lower % sites with plaque at baseline, non-molar teeth, sites with deeper PPD at baseline and sites with fewer occasions of bleeding during the study were associated with greater 6-month reduction in PPD. The variations at each level were reduced markedly with the inclusion of the 6 predictors in the multilevel multiple regression (subject: 79%, tooth: 27%, site: 46%). Conclusion: The use of multilevel analysis enables researchers to incorporate predictor variables measured at different levels in the same model. Multilevel analysis appears to be a powerful statistical tool for the analysis of periodontal data. E-mail:mcmwong@hkucc.hku.hk

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