Robust Transition Models for the Analysis of Longitudinal Drinking Outcomes

描述（由申请人提供）：确定酗酒的有效治疗方法以及过渡性或模式性饮酒的预测因素是NIAAA的重要目标。在临床研究中，适当饮酒结果的推导经常受到争论。主要饮酒和次要非饮酒结果，如情绪和生活质量，都是评估治疗效果的重要结果。然而，常用的自我报告饮酒总结并没有提供关于治疗和时间依赖性合并症对日常饮酒行为影响的信息。为了适当地模拟饮酒和非饮酒结果的演变，以应对这些变量，密集测量的纵向响应的统计方法，应在这种情况下使用。他们应该处理使用各种指标衡量的结果，这些结果取决于多个随时间变化的因素。他们还应该减轻自我报告中固有的测量误差的影响，因为饮酒总结通常是使用基于日历的回忆方法报告的。最后，方法应适用于饮酒和二次非饮酒结果的同时联合建模。目前的统计方法没有在一个统一的框架下处理所有这些问题。目标：使用贝叶斯范式，拟议的研究将开发强大的统计方法，解决所有上述挑战，以评估饮酒和非饮酒行为的治疗效果，并评估饮酒演变的时间依赖性协变量的相关性。软件将免费开发和传播。主题：将在两个数据集上评价统计方法，酒精依赖药物治疗和行为干预联合临床试验（联合收割机）（n=1383）和一项前瞻性合并症研究（n=663），评估酒精和其他物质依赖过程中的重度抑郁症。可用数据和研究设计：收集每日或每周的反应，如饮酒、其他物质使用、情绪、抑郁症状、渴望、压力和生活质量，并将其作为结果进行评估。治疗状态、人口统计学、基线测量和既往精神/健康疾病将作为基线变量，相关医学状态、不良事件和整个研究期间测量的精神疾病（如重度抑郁症）发作将作为模型中的预测因子。这两项研究都有少数民族参与者的代表性，包括妇女的比例很高。重要性：新的统计方法将为酒精研究人员提供丰富的描述，主要饮酒和次要非饮酒结果的行为演变，以应对整个研究过程中的治疗和时间依赖性合并症。 公共卫生相关性：该项目提出了强大的过渡模型，以解决以前没有通过常规汇总统计或简单的纵向模型研究前瞻性酒精结果的关注。通过有效利用数据，拟议的研究有可能从公共卫生的角度产生巨大的影响。长期目标是为酒精研究人员提供更好地了解酒精依赖的随机机制，不仅为更好的研究设计提供信息，而且可能改善预防和控制策略。

DESCRIPTION (provided by applicant): Identifying effective treatments for alcoholism and predictors of transitional or pattern drinking are important goals of the NIAAA. In clinical studies, the derivation of appropriate drinking outcomes is often subject to debate. Both primary drinking and secondary non drinking outcomes, such as mood and quality of life, are important outcomes via which to assess treatment effects. However, commonly used summaries of self-reported drinking do not provide information about the effect of treatment and time dependent comorbidities on daily drinking behavior. To appropriately model the evolution of drinking and non drinking outcomes in response to these variables, statistical methods for densely measured longitudinal responses should be developed for use in this setting. They should handle outcomes that are measured using various metrics and that are dependent on multiple time varying factors. They should also mitigate the effect of measurement error inherent in self-report, as drinking summaries are typically reported using a calendar based method of recall. Finally, methods should lend themselves to simultaneous joint modeling of drinking and secondary nondrinking outcomes. Current statistical methods do not address all of this under a unified framework. Goals: Using a Bayesian paradigm, the proposed study will develop robust statistical methods addressing all of the above challenges for assessing treatment effectiveness on drinking and non drinking behavior, and for assessing the relevance of time dependent covariates on the evolution of drinking. Software will be developed and disseminated freely. Subjects: The statistical methods will be evaluated on two datasets, the Combined Pharmacotherapies and Behavioral Interventions for Alcohol Dependence (COMBINE) clinical trial (n=1383) and a prospective comorbidity study (n=663) assessing major depressive disorder on the course of alcohol and other substance dependence. Available data and study design: Daily or weekly responses such as drinking, other substance use, mood, depressive symptoms, craving, stress, and quality of life were collected and will be evaluated as outcomes. Treatment status, demographics, baseline measures, and prior psychiatric/health disorders will serve as baseline variables, and relevant medical status, adverse events, and onset of psychiatric disorders such as major depressive disorder that were measured throughout the studies will serve as predictors in the models. Both studies have an over representation of minority participants and include women in high proportions. Significance: The new statistical methods will provide alcohol researchers with a rich description of the behavioral evolution of primary drinking and secondary nondrinking outcomes in response to treatment and time dependent comorbidities throughout the course of these studies. PUBLIC HEALTH RELEVANCE: This project proposes robust transition models to address concerns not previously investigated through routine summary statistics or simple longitudinal models for studying prospective alcohol outcomes. The proposed research has the potential to exert tremendous impact from a public health perspective through efficient use of the data. The long term goal is to provide alcohol researchers a better understanding of the stochastic mechanism of alcohol dependence, so as to not only inform better study designs, but potentially improve prevention and control strategies.