Data harmonization and synthesis for mediation and moderation analysis

我的研究重点是发展和评估贝叶斯方法的分析中介模型和结构方程模型（sem）。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效应从一个变量传递到另一个变量（中介）的中间变量，以及提供有关效应发生条件的附加信息的变量（调节者）。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。在数据综合中获得对影响的准确估计有三个主要障碍：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，则同一参数不能在研究之间直接进行比较；2)大多数数据合成方法是针对产生两组、相关性或回归系数的标准化平均差异的简单模型开发的，缺乏经过经验检验的方法来综合更复杂模型（例如，中介模型）的结果；3)数据综合方法的发展在历史上独立于数据协调方法的发展（使用不同的测量工具和/或不同的响应类别获得相同结构的等分），反之亦然。到目前为止，还没有统一的数据协调框架和用于中介和调节分析的数据合成框架。因此，拟议研究计划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，以适应测量工具中研究间差异的各种来源；2)为中介和调节分析中的数据协调和综合创建一个综合的统计框架。如果没有方法有效地综合检验中介和调节研究的结果，现有的研究就不能用于政策和决策。这一建议可能具有开创性，因为它描述了第一个用于数据协调和数据综合的中介者和调节者效应的综合统计框架。每年都有成千上万的社会科学研究对中介和调节者进行研究，根据心理学、流行病学和教育研究等领域的发现，提出的方法有可能改善政策和决策。

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.