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）：半个多世纪以来，心理学家已经理论化了日常情绪（例如压力、负面情绪）和物质使用之间的联系。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与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（例如日记研究）的最新进展催生了一系列旨在测试个体内部的各种情绪-物质使用关系的研究应用。这一重要的进展使人们能够更细致地了解物质使用的病因，而这是人际研究不可能实现的。然而，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缺失（即最令人不安的缺失数据）。首先，这些措施的敏感性，有时甚至是犯罪性质，使得披露存在一定的风险。其次，物质使用的生态评估依赖于醉酒或“兴奋”个体的自我报告。不可忽视的缺失会导致对情绪、物质使用和调节因素之间关系的有偏见的推论。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）即使在存在不可忽略的缺失数据的情况下也能获得无偏参数估计。 LCPMM 在这种情况下发挥了作用，通过考虑辍学模式和结果轨迹与潜在类别变量之间的条件依赖性。汇总类内估计值以获得无偏差的总体估计值。虽然 LCPMM 有希望，但尚未应用于体验采样数据集。拟议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的特征类型和缺失模式进行彻底审查。第二个目标是 LCPMM 框架的扩展，以适应丢失数据的这些类型和模式。最终目标是通过重新分析之前根据有关缺失数据机制的可疑假设进行分析的两个数据集，更严格地测试自我治疗假设。

项目成果

Using a shared parameter mixture model to estimate change during treatment when termination is related to recovery speed.

当终止与恢复速度相关时，使用共享参数混合模型来估计治疗期间的变化。

• 发表时间: 2014-10
• 影响因子: 5.9
• 作者: Gottfredson, Nisha C;Bauer, Daniel J;Baldwin, Scott A;Okiishi, John C
• 通讯作者: Okiishi, John C

Modeling Change in the Presence of Non-Randomly Missing Data: Evaluating A Shared Parameter Mixture Model.

对存在非随机缺失数据的变化进行建模：评估共享参数混合模型。

• 发表时间: 2014-01-01
• 作者: Gottfredson, Nisha C;Bauer, Daniel J;Baldwin, Scott A
• 通讯作者: Baldwin, Scott A