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性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，通常使用的自我报告饮酒摘要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。为了根据这些变量对饮酒和不饮酒结果的演变进行适当建模，应该开发用于密集测量纵向反应的统计方法，以便在这种情况下使用。他们应该处理使用各种度量标准测量的结果，这些结果依赖于多个随时间变化的因素。它们还应该减轻自我报告中固有的测量误差的影响，因为饮酒摘要通常是使用基于回忆的日历方法报告的。最后，方法应该使自己能够同时联合建模饮酒和次要非饮酒结果。目前的统计方法不能在一个统一的框架下解决所有这些问题。目标：使用贝叶斯范式，本研究将开发强大的统计方法来解决上述所有挑战，以评估饮酒和非饮酒行为的治疗效果，并评估饮酒演变中时间相关协变量的相关性。软件将自由开发和传播。受试者：统计方法将在两个数据集上进行评估，一个是酒精依赖的联合药物治疗和行为干预（COMBINE）临床试验（n=1383），另一个是评估酒精和其他物质依赖过程中重度抑郁症的前瞻性合并症研究（n=663）。可用数据和研究设计：收集每日或每周的反馈，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。治疗状况、人口统计、基线测量和先前的精神/健康障碍将作为基线变量，而相关的医疗状况、不良事件和精神障碍（如重度抑郁症）的发作在整个研究中被测量将作为模型的预测因子。这两项研究都有少数民族参与者的过度代表性，其中包括很高比例的女性。意义：新的统计方法将为酒精研究人员在整个研究过程中对治疗和时间依赖性合并症的反应中原发性饮酒和继发性不饮酒结果的行为演变提供丰富的描述。

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.