Aiding Effective Decision Making in Dental Research Using Network Meta-analysis

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DESCRIPTION (provided by applicant): Comparative effectiveness research (CER) of dental procedures relies fundamentally on accurate assessment of treatment efficacy, which is commonly measured by multiple clinical indices. To rank and identify best treatments, those multiples clinical indices need to be combined in a unified manner. Reporting the rank and the probability of being best treatments separately for each outcome, as it is currently done, can often lead to conflicting results, which are not useful for dentists and patients when making treatment decisions. The growing number of treatment options for a given dental condition, as well as the rapid escalation in their costs, has generated an increasing need for scientifically rigorous simultaneous comparisons of treatment procedures in dental clinical practice. Also called mixed or multiple treatments meta- analysis, network meta-analysis (NMA) expands the scope of a conventional pairwise meta-analysis by simultaneously analyzing both direct comparisons of interventions within randomized controlled trials and indirect comparisons across trials. Compared to a traditional meta-analysis that compares two treatments, NMA presents many additional statistical challenges. For example, attempts to compare and rank a large number of treatments, say a dozen, using data from randomized trials that individually compare two (or a few) treatments results in a large amount of missing data since the outcomes for treatments not studied in a particular trial are missing by design. Currently available statistical methods, which are based on treatment contrasts, focus only on relative treatment effect estimates and have several serious limitations. The overall goal of this proposal is to develop cutting-edge statistical methods, and to integrate them into publicly available, easy-to-use software, to enhance NMA in dental research. Specifically, we will develop multivariate Bayesian hierarchical models for multiple mixed outcomes from the perspective of missing data methods with the following three specific aims: 1) to combine multiple mixed endpoints (e.g., binary, categorical and continuous responses) in a unified framework to rank and identify best treatments; 2) to conduct a systematic review and network meta-analysis of interproximal oral hygiene methods in the reduction of clinical indices of inflammation; 3) to produce user-friendly, free, open-source software to facilitate NMAs in dental research and other research areas. We propose to perform empirical assessment of the strengths and weaknesses of the proposed methods through reanalyzing several published NMAs in dental research, extensive simulations, and a real case study on interproximal oral hygiene methods in the reduction of clinical indices of inflammation. Completion of the three aims will substantially advance CER analytical methods for simultaneously comparing multiple treatment procedures across multiple endpoints.