Bayesian nonparametric hierarchical meta-regression: a flexible approach for modeling multiple biases when combining studies of varying quality and different types

贝叶斯非参数分层元回归：在结合不同质量和不同类型的研究时对多重偏差进行建模的灵活方法

• 批准号: 503988801
• 负责人: Privatdozent Dr. Pablo-Emilio Verde
• 金额: --
• 依托单位: Department of Biostatistics
• 依托单位国家: 德国
• 项目类别: Research Grants
• 资助国家: 德国
• 项目状态: 未结题
• 来源: https://gepris.dfg.de/gepris/projekt/503988801?language=en
• 关键词: Bayesian; nonparametric; hierarchical; meta; regression

The objective of this project is to provide a flexible approach for jointly modeling effectiveness and biases in a meta-analysis that combines results from studies of different types (e.g., RCTs, observational studies) and varying quality. We investigate if Bayesian nonparametric methods of the Hierarchical Meta-Regression provide a more data-driven and robust approach against model misspecification in meta-analysis. The hierarchical meta-regression model explicitly distinguishes two sub-models: a sub-model used to handle the data collection process (e.g., modeling internal and external validity bias), and a sub-model used to answer the research questions (e.g., effectiveness, prognosis). The potential advantage of Bayesian nonparametric methos will be investigated in one or both of these sub-models. An R package will be developed based on these methods. The project aims to make a significant contribution to the assessment of health technologies.

这个项目的目标是提供一种灵活的方法，在结合不同类型(例如，随机对照试验、观察性研究)和不同质量的研究结果的荟萃分析中，对有效性和偏差进行联合建模。我们调查了分层Meta回归的贝叶斯非参数方法是否提供了一种针对Meta分析中模型错误指定的更数据驱动和更健壮的方法。分层元回归模型明确区分了两个子模型：用于处理数据收集过程的子模型(例如，模拟内部和外部效度偏差)，以及用于回答研究问题(例如，有效性、预后)的子模型。贝叶斯非参数方法的潜在优势将在这两个子模型中的一个或两个中进行研究。基于这些方法，将开发一个R包。该项目旨在对卫生技术的评估作出重大贡献。