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: 2021
• 资助国家: 加拿大
• 起止时间: 2021-01-01 至 2022-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=742539
• 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.

我的研究重点是用于中介模型和结构方程模型(SEM)分析的贝叶斯方法的发展和评估。这项工作属于NSERC主题MS28，并应用于社会、行为和医学科学。科学研究经常检查将影响从一个变量传递到另一个变量的中间变量(中介)，以及提供有关影响发生条件的额外信息的变量(调节变量)。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所有相关先前研究的结果。在数据合成中获得准确的效果估计有三个主要障碍：1)如果在测量工具、目标人群、分析中使用的协变量和/或研究程序方面存在差异，则相同的参数在研究之间不能直接比较；2)大多数数据合成方法是针对产生两组标准化平均差异、相关性或回归系数的简单模型开发的，并且缺乏用于合成来自较复杂模型(例如中介模型)的结果的经验证的方法，3)数据合成方法的发展历来独立于数据协调方法的发展(将使用不同测量工具和/或不同反应类别获得的同一结构的分数相等)，反之亦然。到目前为止，还没有为调解和调解分析统一数据和综合数据的统一框架。因此，拟议研究方案的长期目标是将中小企业的数据协调和数据合成整合到一个单一的综合统计框架中。拟议研究计划的短期目标是：1)开发和测试数据协调方法，以适应研究之间测量工具差异的各种来源；2)为调解和缓和分析中的数据协调和综合创建一个综合统计框架，如果没有方法有效地综合审查调解人和主持人的研究结果，现有研究就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关于调解人和主持人效应的数据统一和数据合成的综合统计框架。每年在数千项社会科学研究中对调解人和主持人进行审查，拟议的方法有可能根据心理学、流行病学和教育研究等领域的研究结果改进政策和决策。