Bayesian Methods and Software for Patient-Centered Network Meta-Analysis of Binar

用于以患者为中心的二进制网络荟萃分析的贝叶斯方法和软件

基本信息

批准号：
8661112
负责人：
Haitao Chu
金额：
$ 21.65万
依托单位：
UNIVERSITY OF MINNESOTA
依托单位国家：
美国
项目类别：
财政年份：
2013
资助国家：
美国
起止时间：
2013-05-15 至 2016-07-30
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/8661112
关键词：
Address Adverse event Area Bayesian Method Bayesian Modeling Cardiovascular system Caregivers Characteristics Clinical Complex Computer software Conflict (Psychology)Data Data Analyses Data Set Data Sources Development Disease Effectiveness Event Future Goals Health Individual Intervention Journals Language Measures Meta-Analysis Methodology Methods Modeling Odds Ratio Outcome Outcomes Research Parents Patient Preferences Patient-Focused Outcomes Patients Peer Review Performance Phase Property Public Health Randomized Randomized Clinical Trials Randomized Controlled Trials Relative (related person)Relative Risks Reporting Research Research Personnel Research Project Grants Risk Safety Scheme Statistical Methods Testing Treatment Efficacy Treatment Protocols Treatment outcome United States National Institutes of Health Weight Work Writing analytical method base cancer therapy clinical practice comparative effectiveness cost design effectiveness research experience improved innovation patient oriented preference programs public health relevance randomized trial response simulation software development systematic review theories tool treatment effect trial comparing user friendly software web page web site

Address Adverse event Area Bayesian Method Bayesian Modeling Cardiovascular system Caregivers Characteristics Clinical Complex Computer software Conflict (Psychology)Data Data Analyses Data Set Data Sources Development Disease Effectiveness Event Future Goals Health Individual Intervention Journals Language Measures Meta-Analysis Methodology Methods Modeling Odds Ratio Outcome Outcomes Research Parents Patient Preferences Patient-Focused Outcomes Patients Peer Review Performance Phase Property Public Health Randomized Randomized Clinical Trials Randomized Controlled Trials Relative (related person)Relative Risks Reporting Research Research Personnel Research Project Grants Risk Safety Scheme Statistical Methods Testing Treatment Efficacy Treatment Protocols Treatment outcome United States National Institutes of Health Weight Work Writing analytical method base cancer therapy clinical practice comparative effectiveness cost design effectiveness research experience improved innovation patient oriented preference programs public health relevance randomized trial response simulation software development systematic review theories tool treatment effect trial comparing user friendly software web page web site

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项目摘要

DESCRIPTION (provided by applicant): Comparative effectiveness research (CER) relies fundamentally on accurate assessment of treatment efficacy and safety that, ideally, can be tailored to specific patients. The growing number of treatment options for a given condition, as well as the rapid escalation in their costs, has generated an increasing need for scientifically rigorous simultaneous comparisons of multiple treatments in 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 traditional meta-analysis of just two treatments, NMA presents many additional statistical challenges. In particular, a typical randomized trial compares only a few (typically tw) treatments, which intrinsically creates a large amount of missing data when, say, a dozen treatments must be compared simultaneously, 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 other 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 patient-centered NMA. Specifically, we will develop multivariate Bayesian hierarchical models for binary outcomes from the perspective of missing data methods with the following three specific aims: 1) to extend our preliminary work on estimating patient-centered parameters (e.g., absolute risk, risk difference and relative risk) with a single endpoint to allow non-ignorable missingness; 2) to simultaneously model multiple endpoints (e.g. outcomes for efficacy and safety) with proper consideration of non-ignorable missingness; and 3) to incorporate individual patient characteristics. In addition, we propose new methods to measure and detect inconsistency between the direct and indirect evidence, and to borrow strength cautiously from less reliable data sources. We propose to perform empirical assessment of the strengths and weaknesses of these methods through many real data applications and simulations. Completion of the three aims will substantially advance CER analytical methods for comparing multiple treatments across multiple endpoints and tailored to patient characteristics.

描述（由申请人提供）：比较有效性研究（CER）从根本上依赖于对治疗功效和安全性的准确评估，理想情况下，可以针对特定患者量身定制。给定条件的治疗选择的数量越来越多，以及成本的快速升级，对临床实践中多种治疗的科学同时比较越来越需要进行严格的同时比较。网络荟萃分析（NMA）也称为混合或多种治疗方法，通过同时分析随机对照试验中的干预措施的两种直接比较，并在试验中进行间接比较。特别是，一项典型的随机试验仅比较了几种（通常是TW）治疗方法，当时必须同时比较十几个治疗方法，因为设计在特定试验中缺少了未研究的治疗结果。目前基于治疗对比的可用统计方法仅着眼于相对治疗效果估计，并具有其他严重的局限性。该提案的总体目标是开发最先进的统计方法，并将它们集成到公共可用的易于使用的软件中，以增强以患者为中心的NMA。具体而言，我们将从缺少数据方法的角度开发多元贝叶斯分层模型，以使用以下三个特定目的：1）扩展我们在估计以患者为中心的参数（例如，绝对风险，风险差异和相对风险）的初步工作，并允许单个端点缺少不可签名的缺失； 2）到同时对多个终点（例如，有效性和安全性的结果）进行建模，并正确考虑了不可忽视的丢失。 3）纳入个人患者特征。此外，我们提出了新的方法来衡量和检测直接证据和间接证据之间的不一致，并谨慎地从较不可靠的数据源中借用强度。我们建议通过许多真实的数据应用和模拟对这些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，以比较多个终点的多种治疗方法，并根据患者特征量身定制。