Menglu Liang1, Qinshu Lian1, Georgios A Kotsakis2, Bryan S Michalowicz3, Mike T John4, Haitao Chu5. 1. Division of Biostatistics, School of Public Health, University of Minnesota, Minneapolis, MN, USA. 2. Department of Periodontics, UTHealth San Antonio, San Antonio, TX, USA; Department of Global Health, University of Washington, Seattle, WA, USA. 3. Department of Developmental and Surgical Sciences, University of Minnesota, Minneapolis, MN, USA. 4. Department of Diagnostic and Biological Sciences, University of Minnesota, Minneapolis, MN, USA. 5. Division of Biostatistics, School of Public Health, University of Minnesota, Minneapolis, MN, USA. Electronic address: chux0051@umn.edu.
Abstract
OBJECTIVES: Dental research typically targets multiple outcomes. Interdental cleaning devices such as interdental brushes (IB) and water jet devices (WJ) share a sizable portion of the medical device market. However, recommendations for device selection are limited by the conflicting evidence from multiple outcomes in available studies and the lack of an appropriate synthesis approach to summarize evidences taken from multiple outcomes. In particular, both pairwise meta-analyses and single-outcome network meta-analyses can give discordant results. The purpose of this multioutcome, Bayesian network meta-analysis is to introduce this innovative method to the dental research community using data from interdental cleaning device studies for illustrative purposes. METHODS: We reanalyzed a network meta-analysis of interproximal oral hygiene methods in the reduction of clinical indices of inflammation, which included 22 trials assessing 10 interproximal oral hygiene aids. We focused on the primary outcome of gingival inflammation, which was measured by 2 correlated outcome variables, the Gingival Index (GI) and bleeding on probing (BOP). RESULTS: In our previous single-outcome analysis, we concluded that IB and WJ rank high for reducing gingival inflammation while toothpick and flossing rank last. In this multioutcome Bayesian network meta-analysis with equal weight on GI and BOP, the surface under the cumulative ranking curve was 0.87 for WJ and 0.85 for IB. WJ and IB remained ranked as the 2 best devices across different sets of weightings for the GI and BOP. CONCLUSION: In conclusion, multioutcome Bayesian network meta-analysis naturally takes the correlations among multiple outcomes into account, which in turn can provide more comprehensive evidence.
OBJECTIVES: Dental research typically targets multiple outcomes. Interdental cleaning devices such as interdental brushes (IB) and water jet devices (WJ) share a sizable portion of the medical device market. However, recommendations for device selection are limited by the conflicting evidence from multiple outcomes in available studies and the lack of an appropriate synthesis approach to summarize evidences taken from multiple outcomes. In particular, both pairwise meta-analyses and single-outcome network meta-analyses can give discordant results. The purpose of this multioutcome, Bayesian network meta-analysis is to introduce this innovative method to the dental research community using data from interdental cleaning device studies for illustrative purposes. METHODS: We reanalyzed a network meta-analysis of interproximal oral hygiene methods in the reduction of clinical indices of inflammation, which included 22 trials assessing 10 interproximal oral hygiene aids. We focused on the primary outcome of gingival inflammation, which was measured by 2 correlated outcome variables, the Gingival Index (GI) and bleeding on probing (BOP). RESULTS: In our previous single-outcome analysis, we concluded that IB and WJ rank high for reducing gingival inflammation while toothpick and flossing rank last. In this multioutcome Bayesian network meta-analysis with equal weight on GI and BOP, the surface under the cumulative ranking curve was 0.87 for WJ and 0.85 for IB. WJ and IB remained ranked as the 2 best devices across different sets of weightings for the GI and BOP. CONCLUSION: In conclusion, multioutcome Bayesian network meta-analysis naturally takes the correlations among multiple outcomes into account, which in turn can provide more comprehensive evidence.
Authors: Benjamin Rouse; Andrea Cipriani; Qiyuan Shi; Anne L Coleman; Kay Dickersin; Tianjing Li Journal: Ann Intern Med Date: 2016-04-19 Impact factor: 25.391
Authors: Georgios A Kotsakis; Qinshu Lian; Andreas L Ioannou; Bryan S Michalowicz; Mike T John; Haitao Chu Journal: J Periodontol Date: 2018-05 Impact factor: 6.993
Authors: Christopher J Smiley; Sharon L Tracy; Elliot Abt; Bryan S Michalowicz; Mike T John; John Gunsolley; Charles M Cobb; Jeffrey Rossmann; Stephen K Harrel; Jane L Forrest; Philippe P Hujoel; Kirk W Noraian; Henry Greenwell; Julie Frantsve-Hawley; Cameron Estrich; Nicholas Hanson Journal: J Am Dent Assoc Date: 2015-07 Impact factor: 3.634