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

• 批准号: 8066705
• 负责人: Nisha Gottfredson O'Shea
• 金额: $ 0.29万
• 依托单位: UNIV OF NORTH CAROLINA CHAPEL HILL
• 依托单位国家: 美国
• 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/8066705
• 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; Depressed mood; Development; Disclosure; Dropout; Emotional; Emotions; Empirical Research; Etiology; Exhibits; Expectancy; Health; 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; design; diaries; drinking; experience; improved; innovation; novel; prevent; simulation; social cognitive theory; stem; theories; trend

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.

描述（由申请人提供）：超过半个世纪，心理学家已经从理论上证明了日常情绪（例如，压力，负面影响）和物质使用。最近，风险和保护因素，包括应对策略和认知预期已被确定为潜在的调解人的情绪物质使用的关系。多水平统计建模技术和经验抽样方法的最新进展（例如，日记研究）已经导致了一系列的研究应用，这些研究应用被设计为在个体内部水平上测试各种情绪-物质使用关系。这一重要的发展使人们能够更细致地了解物质使用的病因，这是人际研究所不可能做到的。然而，日记研究可能特别容易出现不可否认的数据缺失（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，有时甚至是犯罪性质，使得披露这些措施有些风险。第二，物质使用的生态评估依赖于醉酒或“高”个人的自我报告。不可否认的缺失导致关于情绪、物质使用和调节者之间关系的有偏见的推断。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（LCPORM）来获得无偏的参数估计，即使在存在不可否认的缺失数据。在这种情况下，LCPRisk通过考虑辍学模式和具有潜在类变量的结果轨迹之间的条件依赖关系来工作。将类内估计值汇总以获得无偏总体估计值。虽然很有前途，但LCPSTAR尚未应用于经验采样数据集。拟议的项目有三个具体目标。第一个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，以适应这些类型和模式的缺失数据。最终的目标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，这两个数据集之前是在关于缺失数据机制的可疑假设下分析的。