Modeling non-random missingness in experience sampling research on substance use

描述（由申请人提供）：半个多世纪以来，心理学家已经将日常情绪（如压力、负面影响）和药物使用之间的联系理论化。最近，包括应对策略和认知预期在内的风险和保护因素已被确定为情绪-物质使用关系的潜在调节因素。多层次统计建模技术和经验抽样方法（如日记研究）的最新进展导致了一系列研究应用，旨在测试个人内部水平上各种情绪-物质使用关系。这一重要的发展使我们能够更细致地了解物质使用的病因，这在人际研究中是不可能的。然而，由于许多原因，日记研究可能特别容易出现不可忽视的数据缺失（即最令人不安的数据缺失类型）。首先，这些措施的敏感性，有时甚至是犯罪性质，使得披露具有一定的风险。其次，物质使用的生态评估依赖于醉酒或“高”个体的自我报告。不可忽视的缺失导致了关于情绪、物质使用和调节因子之间关系的偏见推断。近年来，临床试验领域的研究人员利用潜在类模式混合模型（lcpmm）来获得无偏参数估计，即使存在不可忽略的缺失数据。lcpmm通过考虑辍学模式与潜在类别变量的结果轨迹之间的条件依赖关系，在这种情况下发挥了作用。类内估计被汇总以获得无偏的总体估计。lcpmm虽然很有前途，但尚未应用于经验采样数据集。拟议的项目有三个具体目标。首先是对检查物质使用的经验抽样数据集中缺失的特征类型和模式进行彻底审查。第二个目标是扩展LCPMM框架，以适应这些缺失数据的类型和模式。最终目的是通过重新分析两个数据集来更严格地测试自我药物治疗假设，这些数据集之前是在关于缺失数据机制的可疑假设下分析的。公共卫生相关性：拟议的项目将作出独特的实质性和定量贡献。从本质上讲，本项目将可靠地测量日常情绪波动对药物使用行为的影响以及潜在风险和保护因素在此过程中的作用。这一知识将揭示有效设计和实施预防药物滥用干预措施的新方法。实质性分析将为展示和传播数量上的进展提供工具。

DESCRIPTION (provided by applicant): For over half a century, psychologists have theorized a link between daily emotion (e.g., stress, negative affect) and substance use. More recently, risk and protective factors including coping strategies and cognitive expectancies have been identified as potential moderators to the emotion-substance use relationship. Recent advances in multilevel statistical modeling techniques and experience sampling methodology (e.g., diary studies) have resulted in a flurry of research applications designed to test a variety of emotion-substance use relations at the intra-individual level. This important development allows a more nuanced understanding of the etiology of substance use that was not possible with inter-personal studies. However, diary studies may be especially prone to nonignorably missing data (i.e., the most troubling kind of missing data) for a number of reasons. First, the sensitive, and sometimes criminal, nature of the measures makes disclosure somewhat risky. Second, ecological assessments of substance use rely on self reports from intoxicated or "high" individuals. Nonignorable missingness leads to biased inferences regarding the relationship between emotion, substance use, and moderators. Recently, researchers in the area of clinical trials have utilized latent class pattern mixture models (LCPMMs) to obtain unbiased parameter estimates even in the presence of nonignorably missing data. LCPMMs have worked in this context by accounting for conditional dependencies between dropout patterns and outcome trajectories with latent class variables. Within-class estimates are aggregated to obtain unbiased overall estimates. While promising, LCPMMs have not yet been applied to experience sampling datasets. The proposed project has three specific aims. The first is to conduct a thorough review of the characteristic types and patterns of missingness in experience sampling datasets which examine substance use. The second aim is an extension of the LCPMM framework to accommodate these types and patterns of missing data. The final aim is to more rigorously test the self medication hypothesis by reanalyzing two datasets that were previously analyzed under questionable assumptions about the missing data mechanisms. PUBLIC HEALTH RELEVANCE: The proposed project will make unique substantive and quantitative contributions. Substantively, this project will reliably measure the effects that day-to-day emotional fluctuations have on substance use behaviors and the role of potential risk and protective factors in this process. This knowledge will reveal new ways to effectively design and implement interventions to prevent substance abuse. The substantive analysis will provide a vehicle for demonstrating and disseminating quantitative advances.