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

描述(由申请者提供)：半个多世纪以来，心理学家对日常情绪(如压力、负面情绪)和物质使用之间的联系进行了理论化。最近，包括应对策略和认知预期在内的风险和保护因素被确定为情绪-物质使用关系的潜在调节因素。多水平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，旨在测试个体内部水平上的各种情绪-物质使用关系。这一重要的发展使得对物质使用的病因学有了更微妙的理解，这是个人间研究所不可能的。然而，日记研究可能特别容易出现不可忽视的缺失数据(即最令人不安的缺失数据)，原因有很多。首先，这些措施的敏感性质，有时甚至是犯罪性质，使得披露信息具有一定的风险。其次，对物质使用的生态评估依赖于醉酒或“兴奋”的个人的自我报告。不可忽视的缺失会导致对情绪、物质使用和主持人之间的关系做出有偏见的推断。最近，临床试验领域的研究人员利用潜在类别模式混合模型(LCPMM)来获得无偏参数估计，即使在存在不可忽视的缺失数据的情况下也是如此。LCPMM通过考虑辍学模式和带有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。对类内估计进行汇总，以获得无偏见的总体估计。虽然LCPMM很有希望，但它还没有被应用于采样数据集的体验。拟议的项目有三个具体目标。首先是对检验物质使用情况的经验抽样数据集中缺失的特征类型和模式进行彻底的审查。第二个目标是LCPMM框架的扩展，以适应这些类型和模式的缺失数据。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用药假说，这两个数据集之前是在关于缺失数据机制的可疑假设下进行分析的。公共卫生相关性：拟议的项目将作出独特的实质性和量化贡献。实质上，该项目将可靠地衡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这一过程中的作用。这一知识将揭示有效设计和实施干预措施以防止药物滥用的新方法。实质性分析将为展示和传播数量方面的进展提供工具。

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.