Innovative Approaches to Modeling Alcohol Relapse

酒精复吸建模的创新方法

• 批准号: 6560580
• 负责人: Gordon ALAN Marlatt
• 金额: $ 10.84万
• 依托单位: UNIVERSITY OF WASHINGTON
• 依托单位国家: 美国
• 财政年份: 2003
• 资助国家: 美国
• 起止时间: 2003-05-01 至 2005-04-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/6560580
• 关键词: alcoholism /alcohol abuse; alcoholism /alcohol abuse therapy; behavior prediction; behavioral /social science research tag; clinical research; cognition; coping; data management; disease /disorder model; emotions; human data; human therapy evaluation; longitudinal human study; mathematical model; model design /development; outcomes research; relapse /recurrence; statistics /biometry; substance abuse related behavior

DESCRIPTION (provided by applicant): The proposed research will use existing data from the Relapse Replication and Extension Project (RREP) to increase our understanding of the relapse process in individuals who have received treatment for an alcohol use disorder. This dataset includes pre- and post-treatment assessment of drinking behavior, background characteristics, and relapse precipitants in a group of 563 research participants, who were recruited from 15 treatment sites that offered a variety of alcoholism treatment approaches. Using a multi-deterministic framework, the relationship between background variables, risk and protective factors, and situational determinants will be examined with respect to post-treatment drinking outcomes. It is well-documented that a lack of effective coping responses increases relapse risk; and self-efficacy, outcome expectancies, and the abstinence violation effect, are cognitive factors that have been identified as important mediators in the return to heavy drinking following treatment. However, the relationship between these variables in the relapse process has not been clearly established. Focusing on the process of relapse, rather than evaluating relapse as a dichotomous event, the proposed research will examine trajectories of drinking behavior during the first year after treatment for an alcohol use disorder. Specifically, the first goal of the research is to further delineate the relationship between coping behavior, background characteristics, and cognitive factors in the prediction of these trajectories using advanced statistical models, including both catastrophe models and general growth mixture models. The second goal of the proposed research is to test the utility and appropriateness of these advanced modeling techniques in the prediction of post-treatment drinking outcomes. Gaining a better understanding of the relapse process has been a primary goal of addictive behaviors research for the past 30 years. The abundance of treatment outcome data for addictive behaviors makes the proposed research an important and cost-effective contribution to the advancement of our understanding of the relapse process.

描述（由申请人提供）：拟议的研究将使用复发复制和扩展项目（RREP）的现有数据来加深我们对接受酒精使用障碍治疗的个体复发过程的了解。该数据集包括 563 名研究参与者的治疗前和治疗后饮酒行为、背景特征和复发诱因的评估，这些参与者是从 15 个提供各种酗酒治疗方法的治疗点招募的。使用多重确定性框架，将根据治疗后饮酒结果检查背景变量、风险和保护因素以及情境决定因素之间的关系。有充分证据表明，缺乏有效的应对措施会增加复发风险；自我效能、结果预期和戒酒违规效应等认知因素已被确定为治疗后重新酗酒的重要中介因素。然而，这些变量在复发过程中之间的关系尚未明确建立。拟议的研究将重点关注复发的过程，而不是将复发视为二分事件，而是检查复发的轨迹 酒精使用障碍治疗后第一年的饮酒行为。具体来说，该研究的首要目标是进一步描绘应对行为、 使用背景特征和认知因素来预测这些轨迹 先进的统计模型，包括灾难模型和一般增长混合模型 模型。本研究的第二个目标是测试以下方法的实用性和适当性： 这些先进的建模技术可用于预测治疗后饮酒结果。 更好地了解复发过程一直是成瘾研究的主要目标 过去30年的行为研究。大量关于成瘾行为的治疗结果数据使得拟议的研究对增进我们对复发过程的理解做出了重要且具有成本效益的贡献。