Robust Transition Models for the Analysis of Longitudinal Drinking Outcomes

描述(由申请人提供)：确定酒精中毒的有效治疗方法和过渡或模式饮酒的预测因素是NIAAA的重要目标。在临床研究中，适当的饮酒结果的推导往往是有争议的。初次饮酒和继发性非饮酒结果，如情绪和生活质量，都是评估治疗效果的重要结果。然而，通常使用的自我报告饮酒的摘要并不提供关于治疗的影响和依赖时间的共病对日常饮酒行为的影响的信息。为了根据这些变量对饮酒和不饮酒结果的演变进行适当的建模，应该开发用于这种设置的密集测量的纵向反应的统计方法。他们应该处理使用各种衡量标准衡量的结果，以及依赖于多个时变因素的结果。他们还应该减轻自我报告中固有的测量误差的影响，因为饮酒摘要通常使用基于日历的回忆方法来报告。最后，方法应该适合于同时对饮酒和二次不饮酒的结果进行联合建模。目前的统计方法没有在统一的框架下处理所有这些问题。目标：利用贝叶斯范式，拟议的研究将开发强大的统计方法，以应对上述所有挑战，以评估饮酒和非饮酒行为的治疗有效性，并评估时间依赖协变量与饮酒演变的相关性。软件将免费开发和传播。研究对象：统计方法将在两个数据集上进行评估，酒精依赖的联合药物疗法和行为干预(联合)临床试验(n=1383)和评估酒精和其他物质依赖过程中的严重抑郁障碍的前瞻性共病研究(n=663)。可获得的数据和研究设计：收集日常或每周的反应，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/健康障碍将作为基线变量，而在整个研究过程中测量的相关医疗状况、不良事件和精神疾病(如严重抑郁障碍)的发病将作为模型中的预测因素。这两项研究都有过多的少数族裔参与者，并包括高比例的女性。意义：新的统计方法将为酒精研究人员提供丰富的描述，说明在整个研究过程中，初次饮酒和第二次不饮酒结果对治疗和时间依赖性并存的反应的行为演变。

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.