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.

Public Health Relevance

The primary goal of the proposed project is to advance the decision making process in dental research using network meta-analytical methods. The overarching goal of this project is to develop statistical methods and related software for network meta-analysis (NMA) combining multiple outcomes, with a specific application in a systematic review and network meta-analysis of interproximal oral hygiene methods in the reduction of clinical indices of inflammation. The proposed statistical methodology will be broadly applicable to statistical analysis and interpretation of complex data sets arising in NMA with multiple endpoints. It will improve dental research and public health by facilitating the integrated comparison of multiple treatments for dental research, cancer, cardiovascular, infectious and many other diseases.

Agency
National Institute of Health (NIH)
Institute
National Institute of Dental & Craniofacial Research (NIDCR)
Type
Small Research Grants (R03)
Project #
5R03DE024750-02
Application #
9020222
Study Section
Special Emphasis Panel (ZDE1-JR (14))
Program Officer
Clark, David
Project Start
2015-03-01
Project End
2017-02-28
Budget Start
2016-03-01
Budget End
2017-02-28
Support Year
2
Fiscal Year
2016
Total Cost
$145,986
Indirect Cost
$45,986
Name
University of Minnesota Twin Cities
Department
Biostatistics & Other Math Sci
Type
Schools of Public Health
DUNS #
555917996
City
Minneapolis
State
MN
Country
United States
Zip Code
55455
Kotsakis, Georgios A; Lian, Qinshu; Ioannou, Andreas L et al. (2018) A network meta-analysis of interproximal oral hygiene methods in the reduction of clinical indices of inflammation. J Periodontol 89:558-570
Lin, Lifeng; Chu, Haitao (2018) Quantifying publication bias in meta-analysis. Biometrics 74:785-794
Lin, Lifeng; Chu, Haitao (2018) Bayesian multivariate meta-analysis of multiple factors. Res Synth Methods 9:261-272
Ma, Xiaoyue; Lin, Lifeng; Qu, Zhiyong et al. (2018) Performance of Between-study Heterogeneity Measures in the Cochrane Library. Epidemiology 29:821-824
Ma, Xiaoye; Lian, Qinshu; Chu, Haitao et al. (2018) A Bayesian hierarchical model for network meta-analysis of multiple diagnostic tests. Biostatistics 19:87-102
Lin, Lifeng; Chu, Haitao; Murad, Mohammad Hassan et al. (2018) Empirical Comparison of Publication Bias Tests in Meta-Analysis. J Gen Intern Med 33:1260-1267
John, Mike T; Michalowicz, Bryan S; Kotsakis, Georgios A et al. (2017) Network meta-analysis of studies included in the Clinical Practice Guideline on the nonsurgical treatment of chronic periodontitis. J Clin Periodontol 44:603-611
Chen, Yong; Liu, Yulun; Chu, Haitao et al. (2017) A simple and robust method for multivariate meta-analysis of diagnostic test accuracy. Stat Med 36:105-121
Lin, Lifeng; Zhang, Jing; Hodges, James S et al. (2017) Performing Arm-Based Network Meta-Analysis in R with the pcnetmeta Package. J Stat Softw 80:
Zhang, Jing; Chu, Haitao; Hong, Hwanhee et al. (2017) Bayesian hierarchical models for network meta-analysis incorporating nonignorable missingness. Stat Methods Med Res 26:2227-2243

Showing the most recent 10 out of 15 publications