Comparative Effectiveness of Medication vs. CBT in Depressed Low-Income Women

药物治疗与 CBT 对抑郁低收入女性的疗效比较

• 批准号: 7876570
• 负责人: Juned Siddique
• 金额: $ 4.99万
• 依托单位: NORTHWESTERN UNIVERSITY AT CHICAGO
• 依托单位国家: 美国
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-04-01 至 2012-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7876570
• 关键词: Comparative; Effectiveness; Medication; vs.; CBT

DESCRIPTION (provided by applicant): Major depression, a disorder with early onset and often chronic course, imposes a high individual burden of pain, suffering, and disability. Most depression treatment studies include primarily White and middle-class populations so that little is known about the effectiveness of established treatments for ethnic minority and poor individuals. A frequent characteristic of depression studies is that outcomes over time are subject to considerable between-subject heterogeneity due to the fact that patients often follow different trajectories over time. When comparing the effectiveness of different treatments, it is important to identify and take into account these different trajectories because the effectiveness of an intervention may depend on the trajectory class of the participants. Growth mixture modeling (GMM) allows the analyst to estimate several different trajectories over the course of a study. In this way, GMM may do a better job of capturing between-subject variability because it does not require that all individuals follow the same trajectory over time. In this application, we propose to compare the effectiveness of medication versus cognitive behavioral therapy using data from the WECare study, a longitudinal depression treatment study of low-income mostly minority women with depression. Using GMM we will identify and predict response trajectories over the course of the study. Our specific aims are (1) Identify subtypes of clinical response trajectories among WECare subjects and then compare the effectiveness of antidepressant medication and cognitive behavioral therapy within these trajectories. (2) Classify subjects in the various response trajectories in terms of their baseline characteristics including socioeconomic and clinical features to identify which patients are more likely to benefit from a given intervention. (3) Extend existing GMM approaches to handle participants who drop out a study. This research has the potential to identify the most effective depression treatments at the individual level. PUBLIC HEALTH RELEVANCE: Project Narrative: This project will estimate the different trajectories that participants follow in response to treatment for depression and the socioeconomic and clinical features associated with these trajectories. The comparative effectiveness of antidepressant medication versus cognitive behavioral therapy will be estimated within trajectories. The overall goal is to contribute to the development of personalized depression interventions for disadvantaged populations.

描述（由申请人提供）：重度抑郁症是一种早期发作的疾病，通常为慢性病程，给个体带来很高的疼痛、痛苦和残疾负担。大多数抑郁症治疗研究主要包括白色和中产阶级人群，因此对少数民族和贫困人群的有效性知之甚少。抑郁症研究的一个常见特征是，随着时间的推移，结果受到相当大的受试者之间的异质性，因为事实上，患者往往遵循不同的轨迹随着时间的推移。当比较不同治疗的有效性时，重要的是识别和考虑这些不同的轨迹，因为干预的有效性可能取决于参与者的轨迹类别。增长混合模型（GMM）允许分析师在研究过程中估计几个不同的轨迹。以这种方式，GMM可以更好地捕获受试者之间的变异性，因为它不需要所有个体随时间遵循相同的轨迹。在本申请中，我们建议使用WECare研究的数据比较药物治疗与认知行为治疗的有效性，WECare研究是一项针对低收入主要是少数民族抑郁症女性的纵向抑郁症治疗研究。使用GMM，我们将确定和预测在研究过程中的响应轨迹。我们的具体目标是（1）确定WECare受试者的临床反应轨迹的亚型，然后比较抗抑郁药物和认知行为治疗在这些轨迹中的有效性。(2)根据受试者的基线特征（包括社会经济学和临床特征）对不同反应轨迹中的受试者进行分类，以确定哪些患者更有可能从给定的干预措施中获益。(3)扩展现有的GMM方法来处理退出研究的参与者。这项研究有可能在个人层面上确定最有效的抑郁症治疗方法。 公共卫生相关性：项目叙述：该项目将估计参与者在应对抑郁症治疗时遵循的不同轨迹以及与这些轨迹相关的社会经济和临床特征。抗抑郁药物与认知行为治疗的比较有效性将在轨迹内估计。总体目标是为弱势群体制定个性化的抑郁症干预措施。