Outcome data from dental caries clinical trials have a naturally hierarchical structure, with surfaces clustered within teeth, clustered within individuals. Data are often aggregated into the DMF index for each individual, losing tooth- and surface-specific information. If these data are to be analysed by tooth or surface, allowing exploration of effects of interventions on different teeth and surfaces, appropriate methods must be used to adjust for the clustered nature of the data. Multilevel modelling allows analysis of clustered data using individual observations without aggregating data, and has been little used in the field of dental caries. A simulation study was conducted to investigate the performance of multilevel modelling methods and standard caries increment analysis. Data sets were simulated from a three-level binomial distribution based on analysis of a caries clinical trial in Scottish adolescents, with varying sample sizes, treatment effects and random tooth level effects based on trials reported in Cochrane reviews of topical fluoride, and analysed to compare the power of multilevel models and traditional analysis. 40,500 data sets were simulated. Analysis showed that estimated power for the traditional caries increment method was similar to that for multilevel modelling, with more variation in smaller data sets. Multilevel modelling may not allow significant reductions in the number of participants required in a caries clinical trial, compared to the use of traditional analyses, but investigators interested in exploring the effect of their intervention in more detail may wish to consider the application of multilevel modelling to their clinical trial data.

1.
Ahn C, Jung SH, Donner A: Application of an adjusted chi2 statistic to site-specific data in observational dental studies. J Clin Periodontol 2002;29:79-82.
2.
Begg MD: Analysis of correlated responses; in Lesaffre E, Feine J, Leroux BG, Declerck D (eds): Statistical and Methodological Aspects of Oral Health Research. Chichester, Wiley, 2009.
3.
Browne WJ, Draper D: A comparison of Bayesian and likelihood-based methods for fitting multilevel models. Bayesian Anal 2006;1:473-514.
4.
Burnside G, Pine CM, Williamson PR: Statistical aspects of design and analysis of clinical trials for the prevention of caries. Caries Res 2006;40:360-365.
5.
Burnside G, Pine CM, Williamson PR: The application of multilevel modelling to dental caries data. Stat Med 2007;26:4139-4149.
6.
Donner A, Klar N: Design and Analysis of Cluster Randomization Trials in Health Research. London, Arnold, 2000.
7.
Forgie AH, Paterson M, Pine CM, Pitts NB, Nugent ZJ: A randomised controlled trial of the caries-preventive efficacy of a chlorhexidine-containing varnish in high-caries-risk adolescents. Caries Res 2000;34:432-439.
8.
Gilthorpe MS, Maddick IH, Petrie A: Introduction to multilevel modelling in dental research. Community Dent Health 2000;17:222-226.
9.
Goldstein H: Multilevel mixed linear model analysis using iterative generalized least squares. Biometrika 1986;73:43-56.
10.
Goldstein H: Multilevel Statistical Models, ed 3. London, Arnold, 2003.
11.
Goldstein H, Rasbash J: Improved approximations for multilevel models with binary responses. J R Stat Soc Ser A 1996;159:505-513.
12.
Hannigan A, O'Mullane DM, Barry D, Schafer F, Roberts AJ: A re-analysis of a caries clinical trial by survival analysis. J Dent Res 2001;80:427-431.
13.
Hausen H: How to improve the effectiveness of caries-preventive programs based on fluoride. Caries Res 2004;38:263-267.
14.
Ismail AI: Visual and visuo-tactile detection of dental caries. J Dent Res 2004;83:C56-C66.
15.
Macfarlane TV, Worthington HV: Some aspects of data analysis in dentistry. Community Dent Health 1999;16:216-219.
16.
Mancl LA, Hujoel PP, DeRouen TA: Efficiency issues among statistical methods for demonstrating efficacy of caries prevention. J Dent Res 2004;83(Spec No C):C95-C98.
17.
Marinho VC, Higgins JP, Logan S, Sheiham A: Topical fluoride (toothpastes, mouthrinses, gels or varnishes) for preventing dental caries in children and adolescents. Cochrane Database Syst Rev 2003;4:CD002782.
18.
Rasbash J, Steele F, Browne W, Prosser B: A User's Guide to MLwiN Version 2.0. Bristol, Centre for Multilevel Modelling, 2005.
19.
Stephenson J, Chadwick BL, Playle RA, Treasure ET: Modelling childhood caries using parametric competing risks survival analysis methods for clustered data. Caries Res 2010;44:69-80.
20.
Wong MC, Lam KF, Lo EC: Analysis of multilevel grouped survival data with time-varying regression coefficients. Stat Med 2011;30:250-259.
Copyright / Drug Dosage / Disclaimer
Copyright: All rights reserved. No part of this publication may be translated into other languages, reproduced or utilized in any form or by any means, electronic or mechanical, including photocopying, recording, microcopying, or by any information storage and retrieval system, without permission in writing from the publisher.
Drug Dosage: The authors and the publisher have exerted every effort to ensure that drug selection and dosage set forth in this text are in accord with current recommendations and practice at the time of publication. However, in view of ongoing research, changes in government regulations, and the constant flow of information relating to drug therapy and drug reactions, the reader is urged to check the package insert for each drug for any changes in indications and dosage and for added warnings and precautions. This is particularly important when the recommended agent is a new and/or infrequently employed drug.
Disclaimer: The statements, opinions and data contained in this publication are solely those of the individual authors and contributors and not of the publishers and the editor(s). The appearance of advertisements or/and product references in the publication is not a warranty, endorsement, or approval of the products or services advertised or of their effectiveness, quality or safety. The publisher and the editor(s) disclaim responsibility for any injury to persons or property resulting from any ideas, methods, instructions or products referred to in the content or advertisements.
You do not currently have access to this content.