Joint modeling of continuous and binary data in meta-analysis

荟萃分析中连续数据和二进制数据的联合建模

• 批准号: 10793351
• 负责人: Lifeng Lin
• 金额: $ 4.48万
• 依托单位: UNIVERSITY OF ARIZONA
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-12-08 至 2024-11-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10793351
• 关键词: Affect; Attention; Bayesian Modeling; Communicable Diseases; Comparative Effectiveness Research; Computer software; Control Groups; Data; Data Set; Decision Making; Development; Diagnosis; Disease; Disease Outcome; Dissemination and Implementation; Doctor of Philosophy; Evaluation; Event; Evidence Based Medicine; Face; Growth; Guidelines; Healthcare; Heterogeneity; Individual; Instruction; Investigation; Joints; Logistics; Major Depressive Disorder; Malignant Neoplasms; Measures; Medical; Mental Depression; Meta-Analysis; Methods; Modeling; Normal Statistical Distribution; Normalcy; Odds Ratio; Outcome; Outcome Measure; Output; Patients; Performance; Phase; Principal Investigator; Probability; Property; Reporting; Research; Research Design; Research Personnel; Sample Size; Source; Standardization; Statistical Computing; Statistical Methods; Techniques; Uncertainty; Work; clinical practice; design; evidence base; handbook; improved; individual patient; interest; novel; open source; response; simulation; systematic review; theories; tool; treatment group; user-friendly

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.

Meta分析中连续数据和二进制数据的联合建模 主要研究者：林立峰博士 总结 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- 基于医学。他们联合收割机和对比研究结果从个别研究，并得出一种形式的evi- 支持指南和辅助医疗决策的证据。在元分析实践中，研究人员- 最近面临的研究报告相同的结果不同，如连续变量（例如，评分 评定抑郁）或二元变量（例如，被诊断为抑郁症的患者数）。将这些联合收割机 两类研究在同一分析中，一种简单的转换方法被广泛用于处理林分- 标准化的平均差异和比值比。然而，当效应量很大或被削减时，这可能是不准确的。 用于二分二元事件的OFF值是极端的（导致罕见事件）。此外，这种转换 方法建立在传统的荟萃分析框架下，其中研究特定的效应量（例如，日志 比值比）近似于正态分布，研究内方差被视为固定、已知 价值观这些假设在某些情况下可能不合适（例如，小样本），并可 产生误导性的荟萃分析结论。随着统计计算的进步， 结果表明，该方法能较好地分析效应量，充分考虑研究内方差的不确定性， 整合到元结果中。 作为对PA-20-200的回应，该提案旨在开发尖端的统计方法， 连续和二元结果数据，从而提高荟萃分析的效率和普遍性。在这 项目中，我们将：开发贝叶斯分层模型来联合合成连续和二元效应 使用广泛的模拟研究和高质量的真实世界数据集来评估性能 以及开发方便用户的开放源码软件（包括R软件包和SAS软件包 宏）来实现所提出的方法。具体而言，该项目将评估优势和劣势- 使用荟萃分析对各种结果（如抑郁症）的开发模型的优点。拟议 这些方法也广泛适用于许多其他疾病，包括癌症、传染病， 他人模拟研究将精心设计和进行，以便涵盖广泛的集合- 关于具有连续和二元结局的研究数量、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， 工作的例子，使从业人员可以轻松，准确地应用所提出的方法，临床实践， Tice。该项目的产出将直接改善比较有效性研究和循证 医学在不同的医学领域。