Causal mediation analysis in mental health with mediator missingness

项目总结 研究暴露和干预背后的机制对心理健康至关重要 研究；这样做可以帮助确定潜在的干预目标并定制干预措施 优化结果。因果调解分析是这项工作的重要工具。一个重要的但 调解分析中未得到充分认识的挑战是缺少数据。缺失数据无处不在 (即使在高质量的心理健康研究中)，而且在中介分析中更具挑战性 与标准分析相比，这是因为涉及了更多的变量。思念往往是 更糟糕的是，当分析涉及多个调解人和/或在中间阶段收集数据时 时间点(衡量调解人时)受到的关注少于基线和结果 数据收集。由于大多数方法论工作都假设有完整的数据，所以它还不够完善 了解在进行因果调解时如何适当处理丢失的数据 分析。作为消除有效使用因果调解分析的这一障碍的第一步 心理健康研究人员，这个项目将解决中介缺失的问题，一个 实践中常见的问题，也是一个特别重要的问题，以确保其有效性 调解分析。本项目将为此目的开发两类方法：(1) 估计方程方法，我们将把一组完整数据方法中的每一种转换为 如果缺失率模型或 对于给定观测数据的调解器的某些相关功能的模型是正确的；(2)响应 针对流行的多重推算，基于每一种全数据方法，我们将开发 针对该方法和被估计的效果量身定做的有针对性的多重补偿程序， 以最大限度地减少因错误指定归责模型而产生的偏差。该项目寻求方法 这是可以直观解释的，并将特别注意沟通和 将开发的方法传播给心理健康研究人员。为便于说明，请参阅 方法将应用于分析焦虑症的治疗效果和 影响性少数族裔青年自杀倾向的差异分析。

PROJECT SUMMARY Examining the mechanisms behind exposures and interventions is crucial in mental health research; doing so can help identify potential intervention targets and tailor interventions to optimize outcomes. Causal mediation analysis is an important tool for this job. An important but under-appreciated challenge in mediation analysis is missing data. Missing data are ubiquitous (even in high quality mental health studies) and are more of a challenge in mediation analyses than in standard analyses, due to the involvement of more variables. Missingness tends to be worse when the analysis involves multiple mediators and/or when data collection at intermediate time points (when mediators are measured) receives less attention than baseline and outcome data collection. Since most methodological work assumes complete data, it is not yet well understood how to appropriately handle missing data when conducting causal mediation analysis. As the first step in removing this barrier to effective use of causal mediation analysis in mental health researcher, this project will tackle the problem of mediator missingness, a common problem in practice and a particularly important one to tackle for the validity of mediation analyses. This project will develop two classes of methods for this purpose: (1) with the estimating equations approach, we will transform each of a collection of full-data methods to observed-data methods that are doubly robust – consistent if either a missingness model or a model for some relevant function of the mediator given observed data is correct; (2) to respond to the popularity of multiple imputation, based on each full-data method, we will develop targeted multiple imputation procedures tailored to the method and the effects being estimated, to minimize bias due to misspecification of the imputation model. The project seeks methods that can be explained intuitively, and will pay special attention to communicating and disseminating the methods developed to mental health researchers. For illustration, the methods will be applied to an analysis of effects of a treatment for anxiety disorder and an analysis of disparities in suicidality affecting sexual minority youth.