Causal mediation analysis in mental health with mediator missingness

中介缺失的心理健康因果中介分析

• 批准号: 10352521
• 负责人: Trang Quynh Nguyen
• 金额: $ 8.19万
• 依托单位: JOHNS HOPKINS UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-01-14 至 2023-12-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10352521
• 关键词: Adolescent; Adolescent Risk Behavior; Affect; Anxiety; Attention; Child; Code; Collection; Communication Methods; Complex; Data; Data Collection; Educational process of instructing; Ensure; Equation; Evaluation; Intervention; Intuition; Investigational Therapies; Judgment; Learning; Left; Measures; Mediation; Mediator of activation protein; Mental Health; Methodology; Methods; Modeling; National Institute of Mental Health; Observational Study; Occupations; Outcome; Partner in relationship; Performance; Probability; Procedures; Publications; Research; Research Personnel; Research Project Grants; Research Support; Suicide; Testing; Time; Weight; Work; base; bullying; design; flexibility; improved; multimodality; randomized trial; sexual minority; sexual minority youth; simulation; sound; tool; treatment effect; treatment of anxiety disorders

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.

项目摘要 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， 优化结果。因果中介分析是这项工作的重要工具。一个重要但 调解分析中未得到充分重视的挑战是缺少数据。缺失数据无处不在 (even在高质量的心理健康研究中），在中介分析中更具挑战性 因为涉及到更多的变量。失踪往往是 当分析涉及多个介质和/或当数据收集在中间时， 时间点（测量介质时）受到的关注少于基线和结果 数据收集。由于大多数方法学工作都假设数据完整， 了解在进行因果关系调解时如何适当处理缺失数据 分析.作为消除有效使用因果中介分析的障碍的第一步， 心理健康研究人员，这个项目将解决调解人失踪的问题， 实践中常见的问题，也是一个特别重要的问题， 调解分析。本项目将为此目的开发两类方法：（1）与 估计方程的方法，我们将转换每一个完整的数据方法的集合， 双重稳健的数据挖掘方法--如果缺失模型或 模型的一些相关功能的中介给定的观测数据是正确的;（2）响应 由于多重插补的流行，我们将在每种全数据方法的基础上， 针对该方法和所估计的影响量身定制的有针对性的多重插补程序， 最大限度地减少由于插补模型的错误指定而导致的偏倚。该项目寻求方法 可以直观地解释，并将特别注意沟通， 向心理健康研究人员传播所开发的方法。为了说明， 方法将应用于分析治疗焦虑症的效果， 分析影响性少数青年自杀倾向的差异。