Data harmonization and synthesis for mediation and moderation analysis

用于中介和调节分析的数据协调和综合

• 批准号: RGPIN-2021-03432
• 负责人: Miocevic, Milica
• 金额: $ 1.68万
• 依托单位: McGill University
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2022
• 资助国家: 加拿大
• 起止时间: 2022-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=764088
• 关键词: Data; harmonization; synthesis; mediation; moderation

My research focuses on the development and evaluation of Bayesian methods for the analysis of mediation models and structural equations models (SEMs). This work falls under NSERC topic MS28, and is applied in the social, behavioral, and medical sciences. Scientific studies often examine intermediate variables that transmit the effect from one variable to another (mediators), and variables (moderators) that provide additional information about conditions in which effects occur. Advancing the field and using scientific findings to inform policy first requires a synthesis of findings from all relevant previous studies. There are three main obstacles to obtaining accurate estimates of effects in data synthesis: 1) the same parameter is not directly comparable between studies if there are differences in measurement instruments, target populations, covariates used in the analysis, and/or study procedures, 2) most data synthesis methods were developed for simple models that yield standardized mean differences for two groups, correlations, or regression coefficients, and there is a lack of empirically tested methods for synthesizing findings from more complex models (e.g., mediation models), and 3) the developments of methods for data synthesis have historically proceeded independently of developments of methods for data harmonization (equating scores on the same construct obtained using different measurement instruments and/or different response categories) and vice-versa. As of now there is no unified framework for data harmonization and data synthesis for mediation and moderation analyses. Therefore, the long-term objective of the proposed research program is to integrate data harmonization and data synthesis for SEMs in a single comprehensive statistical framework. The short-term objectives of the proposed research program are to: 1) Develop and test data harmonization methods that can accommodate various sources of between-study differences in measurement instruments 2) Create an integrated statistical framework for data harmonization and synthesis in mediation and moderation analyses Without methods to effectively synthesize findings from studies that examine mediators and moderators, existing research cannot be used for policy and decision making. This proposal is potentially ground-breaking because it describes the first integrated statistical framework for data harmonization and data synthesis for mediator and moderator effects. Mediators and moderators are examined in thousands of social science studies every year and the proposed methods have the potential to improve policy and decision-making based on findings in fields ranging from psychology to epidemiology and education research.

我的研究主要集中在中介模型和结构方程模型（SEM）分析的贝叶斯方法的开发和评估。这项工作福尔斯属于NSERC主题MS 28，并应用于社会，行为和医学科学。科学研究经常检查将效应从一个变量传递到另一个变量的中间变量（介质），以及提供有关效应发生条件的额外信息的变量（调节剂）。推进这一领域并利用科学发现为政策提供信息，首先需要综合以往所有相关研究的结果。 在数据综合中获得准确的效应估计数有三个主要障碍：1）如果测量工具、目标人群、分析中使用的协变量和/或研究程序存在差异，则研究之间的相同参数不具有直接可比性，2）大多数数据合成方法都是针对简单模型开发的，这些模型可以产生两组的标准化平均差异、相关性或回归系数，并且缺乏用于综合来自更复杂模型的发现的经经验检验的方法（例如，调解模式）; 3）数据综合方法的发展历来独立于数据统一方法的发展（将使用不同测量工具和/或不同答复类别获得的同一结构的分数等同起来），反之亦然。到目前为止，还没有统一的数据协调和数据综合框架，用于调解和调节分析。因此，拟议的研究计划的长期目标是在一个单一的综合统计框架中整合中小型企业的数据协调和数据综合。拟议研究计划的短期目标是：1）开发和测试数据协调方法，可以适应各种来源的研究间差异的测量工具2）创建一个综合的统计框架，数据协调和综合的调解和适度分析没有方法来有效地综合研究结果，检查调解人和主持人，现有的研究不能用于政策和决策。这一提议可能具有开创性，因为它描述了第一个统一数据和综合数据以实现中介和调节效应的综合统计框架。每年有数千项社会科学研究对调解人和主持人进行审查，所提出的方法有可能根据从心理学到流行病学和教育研究等领域的研究结果改进政策和决策。