Promoting Rapid Uptake of Multilevel Latent Class Modeling via Best Practices: Investigating Heterogeneity in Daily Substance Use Patterns

项目总结 年轻成年大学生中的药物使用仍然是一个主要的公共卫生问题，最近 非医用Adderall的使用、尼古丁的蒸发以及酒精和大麻的联合使用的增加。风景名胜 随着新的使用模式(如蒸发)和快速的立法变化，物质使用继续发展。每天 使用行为可以有多种形式，由使用的物质的数量和类型、使用方式和 使用量。此外，日常使用模式可能与日级心理社会因素(例如，动机， 个人特征(例如，性别、种族/族裔、经济压力)。使用多个 一天内的物质(“共同使用”)越来越普遍，并增加了急性和慢性的风险。 后果，但研究还不充分。数量空前的高质量、集约化的纵向 有关药物使用和相关风险因素的数据(ILD)包含关键信息，有助于解释 物质使用病因学并为下一代干预措施的发展提供信息，这些干预措施是为 那些需要它的人，当他们需要的时候。然而，分析ILD的高级方法-包括多水平 需要潜在类别分析(MLCA)和多水平潜在转变分析(MLTA)来表征 在日常生活中展现的使用模式的异质性，并确定新的日级干预目标。 这些方法使研究人员能够发现临床上相关的物质使用行为模式和日常生活 与问题使用模式相关的日常生活中嵌入的动态风险因素， 以及个人层面的特征，表明这些风险因素对谁来说最突出。然而，MLCA和 MLTA在ILD分析中的使用不足，部分原因是缺乏对应用研究人员的指导。指导原则 一个社会生态框架，我们将评估、应用和传播MLCA和MLTA来分析ILD， 考虑四种指定模型的方法：边际建模、完全参数随机效果模型、 公共因素模型，以及随机效应的非参数方法。我们将把最佳实践转化为 将MLCA(目标1)和MLTA(目标2)应用于ILD关于物质使用和联合使用的问题。我们将协调开展 对大学生(3CAM：n=343,15798人/日； 项目自身：n=2068，33,722人日)。经验分析将理清个人层面的风险因素(例如， 特质焦虑)和动态风险因素(例如早晨情绪)，以隔离潜在的、动态的干预 目标。我们将检查个人特征(例如，种族/民族)，作为调查差异的调节因素 并确定特定于群体的机制。最大化我们的整体影响(目标3)，并确保广泛、严谨 采用MLCA和MLTA，我们建议开展广泛的传播活动，包括在线 教学材料、代码库、研讨会和会议。使用这些工具进行研究的结果 技术将指导预防科学家和临床医生设计针对最显著的 在干预的最佳时间针对特定个人的危险因素。

PROJECT SUMMARY Substance use among young adult college students remains a major public health concern, with recent escalations in nonmedical Adderall use, vaping nicotine, and co-use of alcohol and cannabis. The landscape of substance use continues to evolve, with new modes of use (e.g., vaping) and rapid legislative changes. Daily use behaviors can take many forms, defined by number and type of substances used, mode of use, and quantity used. Further, daily use patterns may be related to both day-level psychosocial factors (e.g., motives, affect, stress) and person-level characteristics (e.g., sex, race/ethnicity, financial strain). Use of multiple substances within a day (“co-use”) is increasingly prevalent and confers high risk for acute and chronic consequences, but is insufficiently studied. The unprecedented amount of high-quality, intensive longitudinal data (ILD) on substance use and associated risk factors holds critical information that can help explain substance use etiology and inform the development of the next generation of interventions that are tailored to those who need it, when they need it. However, advanced methods for analyzing ILD—including multilevel latent class analysis (MLCA) and multilevel latent transition analysis (MLTA)—are needed to characterize the heterogeneity of patterns of use that unfold in daily life and to identify novel day-level intervention targets. These methods enable researchers to discover clinically relevant substance use behavior patterns and day-to- day transitions, dynamic risk factors embedded in daily life that are associated with problematic use patterns, and person-level characteristics that indicate for whom these risk factors are most salient. Yet MLCA and MLTA are underused for ILD analysis, in part due to the dearth of guidance for applied researchers. Guided by a socio-ecological framework, we will evaluate, apply, and disseminate MLCA and MLTA to analyze ILD, considering four ways to specify models: marginal modeling, a fully parametric random effects model, a common factor model, and a non-parametric approach for random effects. We will translate best practices for applying MLCA (Aim 1) and MLTA (Aim 2) to ILD on substance use and co-use. We will conduct coordinated MLCA and MLTA of daily data from two large studies of college students (3CAM: n=343, 15,798 person-days; Project SELF: n=2068, 33,722 person-days). Empirical analyses will disentangle person-level risk factors (e.g., trait anxiety) and dynamic risk factors (e.g., morning affect) to isolate potential novel, dynamic intervention targets. We will examine individual characteristics (e.g., race/ethnicity) as moderators to investigate disparities and identify group-specific mechanisms. To maximize our overall impact (Aim 3) and ensure broad, rigorous adoption of MLCA and MLTA, we propose a broad range of dissemination activities to include online instructional material, a code repository, workshops, and a conference. Findings from studies using these techniques will guide prevention scientists and clinicians in designing interventions targeting the most salient risk factors for specific individuals at the best time for intervention.