Statistical Modeling of Medication and Placebo Effects

药物和安慰剂效应的统计模型

• 批准号: 8266870
• 负责人: Robert Loren Stout
• 金额: $ 10.34万
• 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 2014-04-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8266870
• 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. PUBLIC HEALTH RELEVANCE: This study uses advanced statistical methods to tease apart how medications, placebo effects, and other factors affect drinking during and after treatment. This helps researchers how quickly medications begin to help, as well as how quickly their effects taper off, which will help us engineer better combinations of interventions to help people with addictive disorders.

描述（由申请人提供）：本项目的目标是开发一种统计工具，以阐明药物、安慰剂和其他相关因素如何影响治疗结果的时间过程。它解决了RFA的目标，即“开发和应用先进的纵向统计技术来描述酒精临床试验过程中安慰剂反应如何变化，并探索这些反应轨迹中的异质性来源。“标准的统计方法，如线性模型，并不适合研究影响治疗期间和治疗后结果的非常动态的过程。基于对药物和安慰剂效应的时间过程的理论合理假设，我们提出了一个动态非线性统计模型。该模型使用药物依从性和其他时变信息来预测治疗期间和治疗后的饮酒情况。该模型被应用于一个随机的半样本的数据从项目联合收割机，一个大型的多站点酒精治疗研究。基于联合收割机主要结果分析的线性模型解释了饮酒随时间变化的6%;非线性模型能够解释87%。非线性模型需要进一步发展，以提高其适合的时间过程中的结果，并纳入协变量。然后将在联合收割机数据的另一半样本上进行验证。此外，还将使用来自Project Predict的数据再次进行验证，该研究与在德国进行的Project联合收割机平行。对结果的时间过程进行建模，虽然在技术上具有挑战性，但可以区分药物，安慰剂和其他对结果的重要影响。这反过来又使人们能够更清楚地确定药物在哪里成功以及在哪里失败。这项研究中开发的工具中的信息将有助于我们设计更好的干预方案，以改善成瘾性疾病患者的健康状况。 公共卫生关系：这项研究使用先进的统计方法来区分药物、安慰剂效应和其他因素如何影响治疗期间和治疗后的饮酒。这有助于研究人员了解药物开始起作用的速度，以及它们的作用逐渐消失的速度，这将有助于我们设计更好的干预组合来帮助成瘾性疾病患者。