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

使用网络元分析帮助牙科研究中的有效决策

• 批准号: 8806160
• 负责人: Haitao Chu
• 金额: $ 14.62万
• 依托单位: UNIVERSITY OF MINNESOTA
• 依托单位国家: 美国
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-03-01 至 2017-02-28
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8806160
• 关键词: Address; Area; Bayesian Modeling; Cardiovascular system; Case Study; Clinical; Complex; Computer software; Conflict (Psychology); Data; Data Analyses; Data Set; Decision Making; Dental; Dental Care; Dental Hygiene; Dental Research; Dentists; Development; Disease; Event; Future; Gingiva; Goals; Inflammation; Intervention; Journals; Language; Lead; Malignant Neoplasms; Measures; Meta-Analysis; Methodology; Methods; National Institute of Dental and Craniofacial Research; Odds Ratio; Oral health; Outcome; Patient Preferences; Patients; Peer Review; Performance; Phase; Plant Roots; Probability; Procedures; Process; Property; Public Health; Publishing; Randomized; Randomized Controlled Trials; Relative (related person); Relative Risks; Reporting; Research; Research Personnel; Research Project Grants; Risk; Scheme; Statistical Methods; Testing; Time; Tissue Grafts; Tissues; Treatment Efficacy; Treatment Protocols; Treatment outcome; Weight; Work; Writing; analytical method; base; clinical practice; comparative effectiveness; cost; design; effectiveness research; health data; improved; indexing; interest; open source; public health relevance; randomized trial; response; simulation; software development; systematic review; theories; tool; treatment effect; trial comparing; user friendly software; user-friendly; web page; web site

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.

描述（由申请人提供）：牙科程序的比较有效性研究（CER）从根本上依赖于对治疗效果的准确评估，这通常由多个临床指数衡量。为了排名和确定最佳治疗方法，这些倍数需要以统一的方式组合。按照目前完成的每一个结果，报告单独做出最佳治疗的等级和概率通常会导致矛盾的结果，这对牙医和患者做出治疗决策时没有用。给定牙科疾病的治疗选择的数量越来越多，以及成本的迅速升级，在牙科临床实践中对科学严格同时进行治疗程序的比较产生了越来越多的需求。网络荟萃分析（NMA）也称为混合或多种处理元分析，通过同时分析随机对照试验中的两种直接比较，并在试验中进行间接比较，从而扩大了常规成对荟萃分析的范围。与比较两种治疗方法的传统荟萃分析相比，NMA提出了许多其他统计挑战。例如，尝试使用随机试验中的数据分别比较两种（或几种）治疗的数据，试图比较和对大量治疗方法进行比较，例如十二个治疗方法，因为设计未在特定试验中未研究的治疗结果进行大量丢失的数据。目前基于治疗对比的可用统计方法仅着眼于相对治疗效应估计，并有一些严重的局限性。该提案的总体目标是开发最先进的统计方法，并将其集成到公开可用的，易于使用的软件中，以增强牙科研究中的NMA。具体而言，我们将从缺失的数据方法的角度以以下三个具体目的开发多元贝叶斯分层模型，以用于多种混合结果：1）将多个混合终点（例如二进制，分类和连续响应）组合到统一的框架中，以等级和确定最佳处理； 2）在炎症临床指数减少临床指标方面，对跨二介粒卫生方法进行系统审查和网络荟萃分析； 3）生产用户友好，免费的开源软件，以促进牙科研究和其他研究领域的NMA。我们建议通过在牙科研究，广泛的模拟中重新分析拟议方法的优势和缺点，对拟议方法的优势和劣势进行经验评估。这三个目标的完成将大大提高CER分析方法，以同时比较多个终点的多个治疗程序。