Statistical Modeling of Medication and Placebo Effects

药物和安慰剂效应的统计模型

• 批准号: 8468969
• 负责人: Robert Loren Stout
• 金额: $ 9.69万
• 依托单位: PACIFIC INSTITUTE FOR RES AND EVALUATION
• 依托单位国家: 美国
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-05-12 至 2015-04-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8468969
• 关键词: Accounting; Address; Affect; Aftercare; Alcohols; Behavior Therapy; Clinical Trials; Data; Dependency; Development; Dropout; Emotions; Engineering; European; Event; Exhibits; Germany; Goals; Growth; Health; Heterogeneity; Intervention; Light; Linear Models; Medication Management; Methodology; Modeling; Motivation; Naltrexone; Non-linear Models; Nonlinear Dynamics; Outcome; Persons; Pharmaceutical Preparations; Placebo Effect; Placebos; Process; Randomized; Research; Research Personnel; Sampling; Site; Source; Statistical Methods; Statistical Models; Techniques; Therapeutic Intervention; Time; Treatment outcome; Validation; acamprosate; addiction; alcohol abuse therapy; base; compliance behavior; design; drinking; drinking behavior; follow-up; improved; medication compliance; pressure; primary outcome; response; tool

DESCRIPTION (provided by applicant): The goal of this project is to develop a statistical tool to shed light on how the time course of treatment outcome is affected by medications, placebos, and other relevant factors. It addresses the RFA goal to "Develop and apply advanced longitudinal statistical techniques to describe how the placebo response changes over the course of alcohol clinical trials and explore sources of heterogeneity in these response trajectories." Standard statistical methods such as linear modeling are not well suited for studying the very dynamic processes that affect outcome during and after treatment. Based on theoretically-justified assumptions about the time course of both medication and placebo effects, we propose a dynamic nonlinear statistical model. This model uses medication compliance and other time varying information to predict drinking during and after treatment. This model was applied to a random half-sample of data from Project COMBINE, a large multi-site alcohol treatment study. A linear model based on the COMBINE primary outcome analyses accounted for 6 percent of the variance of drinking over time; the nonlinear model was able to account for 87 percent. The nonlinear model requires further development to improve its fit to the time course of outcome, and to incorporate covariates. It will then be validated on the other half-sample of the COMBINE data. Furthermore, it will be validated again using data from Project Predict, a study parallel to Project COMBINE that was conducted in Germany. Modeling the time course of outcome, while technically challenging, makes it possible to differentiate between medication, placebo, and other important effects on outcome. This in turn makes it possible to ascertain more clearly where medications succeed as well as where they falter. The information from the tool developed in this study will help us to engineer better intervention packages to improve the health of persons with addictive disorders.

描述（由申请人提供）：该项目的目的是开发一种统计工具，以阐明治疗结果的时间过程如何受到药物，安慰剂和其他相关因素的影响。它解决了RFA的目标，即“开发和应用先进的纵向统计技术，以描述安慰剂反应在酒精临床试验过程中如何变化，并探索这些反应轨迹中的异质性来源。”诸如线性建模之类的标准统计方法不适合研究影响治疗期间和后结果的非常动态的过程。基于理论上对药物和安慰剂效应的时间过程的假设，我们提出了一个动态的非线性统计模型。该模型使用药物合规性和其他时间变化的信息来预测治疗期间和之后的饮酒。该模型被应用于Project Combine的随机半样本，这是一项大型多站点酒精治疗研究。基于组合主要结果分析的线性模型占饮酒方差的6％。非线性模型能够占87％。非线性模型需要进一步的发展，以提高其对结果的适合度，并结合协变量。然后将在组合数据的另一半样本上进行验证。此外，它将使用Project Predive的数据再次验证，这是一项与在德国进行的Project Combine的研究。在技​​术上具有挑战性的同时，对结果的时间过程进行建模使得有可能区分药物，安慰剂和对结果的其他重要影响。反过来，这使得可以更清楚地确定药物成功以及在哪里步履蹒跚的地方。这项研究中开发的工具的信息将帮助我们改善更好的干预软件包，以改善患有上瘾疾病的人的健康。