Does Classification of Composites for Network Meta-analyses Lead to Erroneous Conclusions?

OBJECTIVES   Composites can be classified differently, according to manufacturer information, filler particle size, resin-monomer base, or viscosity, for example. Using clinical trial data, network meta-analyses aim to rank different composite material classes. Dentists then use these ranks to decide whether to use specific materials. Alternatively, annual failure rates (AFRs) of materials can be assessed, not requiring any classification for synthesis. It is unclear whether different classification systems lead to different rankings of the same material (ie, erroneous conclusions). We aimed to evaluate the agreement of material rankings between different classification systems. METHODS   A systematic review was performed via MEDLINE, Cochrane Central Register of Controlled Trials, and EMBASE. Randomized controlled trials published from 2005-2015 that investigated composite restorations placed in load-bearing cavitated lesions in permanent teeth were included. Network meta-analyses were performed to rank combinations of composite classes (according to manufacturer, filler particle size, resin-monomers, viscosity) and adhesives. Material combinations were additionally ranked using AFRs. RESULTS   A total of 42 studies (6088 restorations, 2325 patients) were included. The ranking of most material class combinations showed significant agreement between classifications ( R2 ranged between 0.03 and 0.56). Comparing material combinations using AFRs had low precision and agreement with other systems. AFRs were significantly correlated with follow-up periods of trials. CONCLUSION   There was high agreement between rankings of identical materials in different classification systems. Such rankings thus allow cautious deductions as to the performance of a specific material. Syntheses based on AFRs might lead to erroneous results because AFRs are determined by follow-up periods and have low precision.

[1]  Julian P T Higgins,et al.  A case study of multiple-treatments meta-analysis demonstrates that covariates should be considered. , 2009, Journal of clinical epidemiology.

[2]  S Dias,et al.  Checking consistency in mixed treatment comparison meta‐analysis , 2010, Statistics in medicine.

[3]  Dimitris Mavridis,et al.  Network meta‐analysis models to account for variability in treatment definitions: application to dose effects , 2013, Statistics in medicine.

[4]  Denis Bourgeois,et al.  The global burden of oral diseases and risks to oral health. , 2005, Bulletin of the World Health Organization.

[5]  Andrew Gelman,et al.  General methods for monitoring convergence of iterative simulations , 1998 .

[6]  D. Manton,et al.  Minimal intervention dentistry for managing dental caries - a review: report of a FDI task group. , 2012, International dental journal.

[7]  Choice of comparator in restorative trials: A network analysis. , 2015, Dental materials : official publication of the Academy of Dental Materials.

[8]  M. Sculpher,et al.  Bayesian methods for evidence synthesis in cost-effectiveness analysis , 2012, PharmacoEconomics.

[9]  F. Schwendicke,et al.  Directly Placed Restorative Materials , 2016, Journal of dental research.

[10]  Georgia Salanti,et al.  Graphical methods and numerical summaries for presenting results from multiple-treatment meta-analysis: an overview and tutorial. , 2011, Journal of clinical epidemiology.

[11]  Deborah M Caldwell,et al.  Simultaneous comparison of multiple treatments: combining direct and indirect evidence , 2005, BMJ : British Medical Journal.

[12]  F. Schwendicke,et al.  Design and Validity of Randomized Controlled Dental Restorative Trials , 2016, Materials.

[13]  Huseyin Naci,et al.  Is network meta-analysis as valid as standard pairwise meta-analysis? It all depends on the distribution of effect modifiers , 2013, BMC Medicine.

[14]  Alex J. Sutton,et al.  Evidence Synthesis for Decision Making 2 , 2013, Medical decision making : an international journal of the Society for Medical Decision Making.

[15]  Rhiannon K Owen,et al.  Network meta-analysis: development of a three-level hierarchical modeling approach incorporating dose-related constraints. , 2015, Value in health : the journal of the International Society for Pharmacoeconomics and Outcomes Research.

[16]  Nicky J Welton,et al.  Automating network meta‐analysis , 2012, Research synthesis methods.

[17]  P. Petersen,et al.  Global goals for oral health 2020. , 2003, International dental journal.