Joint modeling of continuous and binary data in meta-analysis

Meta分析中连续数据和二进制数据的联合建模 主要研究员：林立峰，博士。 摘要 系统评价和荟萃分析是比较有效性研究和证据的关键工具- 基础医学。他们结合和对比来自个别研究的研究结果，并得出一种形式的事件-- Dence支持指导方针并帮助医疗决策。在荟萃分析的实践中，研究人员经常- QUERY FACE研究以不同的方式报告相同的结果，例如连续变量(例如 评估抑郁)或二元变量(例如，诊断为抑郁的患者的计数)。要将这些结合在一起 在同一分析的两种类型的研究中，一种简单的换算方法已被广泛使用来处理标准 计算平均差值和赔率比。然而，当效果尺寸很大或被切割时，这可能是不准确的- 二元事件的OFF值是极端的(导致罕见事件)。此外，此转换 方法建立在传统的元分析框架下，其中研究特定的效应大小(例如，LOG 优势比)近似于正态分布，研究内方差被视为固定的、已知的 价值观。这些假设在某些情况下可能不合适(例如，样本量较小)，并且可能 得出误导性的荟萃分析结论。随着统计计算的进步，精确分布 可以适当地分析影响措施，并且可以充分考虑研究内方差中的不确定因素 包含在元结果中。 针对PA-20-200，这项建议旨在开发尖端的统计方法，以结合各种统计方法。 不连续的和二元的结果数据，从而提高了Meta分析的效率和泛化能力。在这 项目，我们将：开发贝叶斯分层模型来联合合成连续和二元效应 衡量标准；使用广泛的模拟研究和高质量的真实数据集来评估性能 建议的方法；开发用户友好的开源软件(包括R包和SAS 宏)来实现所建议的方法。具体地说，这个项目将评估优势和弱点- 通过对抑郁症等不同结果进行荟萃分析，对开发的模型进行风险评估。建议数 这些方法也广泛适用于许多其他疾病，包括癌症、传染病和 其他。模拟研究将经过精心设计和进行，以涵盖范围广泛的布景- 关于具有连续和二元结果的研究的数量、样本量、事件发生率、 我们开发的用户友好的开源软件将包括详细的说明和 工作实例，以便从业者可以轻松而准确地将所提出的方法应用于临床实践。 蒂斯。该项目的成果将直接改善比较有效性研究和循证 关于不同医学主题的医学。

Joint Modeling of Continuous and Binary Data in Meta-Analysis Principal Investigator: Lifeng Lin, Ph.D. Summary Systematic reviews and meta-analyses are critical tools for comparative effectiveness research and evidence- based medicine. They combine and contrast research findings from individual studies and derive a form of evi- dence to underpin guidelines and aid medical decision making. In meta-analysis practice, researchers fre- quently face studies that report the same outcome differently, such as a continuous variable (e.g., scores for rating depression) or a binary variable (e.g., counts of patients diagnosed with depression). To combine these two types of studies in the same analysis, a simple conversion method has been widely used to handle stand- ardized mean differences and odds ratios. However, this may be inaccurate when effect sizes are large or cut- off values for dichotomizing binary events are extreme (leading to rare events). In addition, this conversion method is built under the conventional framework of meta-analysis, where study-specific effect sizes (e.g., log odds ratios) are approximated to normal distributions and within-study variances are treated as fixed, known values. These assumptions may not be appropriate in some situations (e.g., small sample sizes) and could produce misleading meta-analysis conclusions. With advances in statistical computing, the exact distributions of effect measures could be properly analyzed, and the uncertainties in within-study variances could be fully incorporated in meta-results. In response to PA-20-200, this proposal aims at developing cutting-edge statistical methods for combining con- tinuous and binary outcome data and thus improving the efficiency and generalizability of meta-analysis. In this project, we will: develop Bayesian hierarchical models to jointly synthesize continuous and binary effect measures; use extensive simulation studies and high-quality real-world datasets to evaluate the performance of the proposed methods; and develop user-friendly, open-source software (including R packages and SAS macros) to implement the proposed methods. Specifically, this project will evaluate the strengths and weak- nesses of the developed models using meta-analyses on various outcomes such as depression. The proposed methods are also broadly applicable for many other diseases, including cancers, infectious diseases, among others. The simulation studies will be carefully designed and conducted so that they cover a wide range of set- tings, with respect to the number of studies with continuous and binary outcomes, sample sizes, event rates, heterogeneity, etc. Our developed user-friendly, open-source software will include detailed instructions and worked examples, so that practitioners can easily and accurately apply the proposed methods to clinical prac- tice. The output of this project will directly improve comparative effectiveness research and evidence-based medicine on diverse medical topics.