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

物质使用经验抽样研究中的非随机缺失建模

基本信息

批准号：
7672634
负责人：
Nisha Gottfredson O'Shea
金额：
$ 2.98万
依托单位：
UNIV OF NORTH CAROLINA CHAPEL HILL
依托单位国家：
美国
项目类别：
财政年份：
2009
资助国家：
美国
起止时间：
2009-05-01 至 2011-06-07
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/7672634
关键词：
Accounting Affect Affective Alcohol or Other Drugs use Alcohols Area Behavior Characteristics Clinical Trials Cognitive Coping Skills Data Data Analyses Data Set Dependency Development Disclosure Dropout Emotional Emotions Empirical Research Etiology Exhibits Expectancy Individual Intervention Investigation Knowledge Linear Models Link Measures Methodology Methods Modeling Nature Outcome Patient Self-Report Pattern Persons Plague Plant Roots Prevalence Process Psychologist Recommendation Reporting Research Research Design Research Personnel Risk Role Sampling Self Medication Statistical Methods Statistical Models Stress Substance abuse problem Techniques Testing Time Variant Work base coping depressed design diaries drinking experience improved innovation novel prevent public health relevance simulation social cognitive theory stem theories trend

项目摘要

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.

描述（由申请人提供）：在半个世纪以上，心理学家将日常情绪（例如压力，负面影响）和使用物质使用之间的联系。最近，包括应对策略和认知预期的风险和保护因素已被确定为情感实质使用关系的潜在主持人。多层次统计建模技术和经验抽样方法（例如日记研究）的最新进展导致了一系列旨在在个体内级别测试各种情感实质性使用关系的研究应用。这种重要的发展使得对人际关系研究是不可能的对物质使用的病因的更加细微的理解。但是，由于多种原因，日记研究可能特别容易出现不可忽视的数据（即最令人不安的丢失数据）。首先，这些措施的敏感性，有时甚至是犯罪的性质使披露有些冒险。其次，对物质使用的生态评估依赖于醉酒或“高”个体的自我报告。令人垂涎的丢失导致有关情绪，吸毒和主持人之间关系的有偏见的推论。最近，临床试验领域的研究人员也利用了潜在的类模式混合模型（LCPMMS）来获得无偏见的参数估计值，即使在存在非不于缺失的数据的情况下。 LCPMM在此上下文中使用了潜在类变量的辍学模式和结果轨迹之间的条件依赖性。总体估计值汇总以获得公正的总体估计。尽管有希望，但尚未应用LCPMM来体验抽样数据集。拟议的项目具有三个具体目标。首先是对经验抽样数据集中的特征类型和缺失的特征类型和模式进行详尽的审查，这些数据集检查了使用物质的使用。第二个目的是扩展LCPMM框架，以适应这些类型和丢失数据的模式。最终目的是通过重新分析两个数据集，这些数据集以前对缺失的数据机制的可疑假设进行了分析，以更严格地检验自药假说。公共卫生相关性：拟议的项目将做出独特的实质性和定量贡献。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以及潜在风险和保护因素在此过程中的作用的影响。这些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。实质性分析将为证明和传播定量进步提供工具。