Characterizing Placebo Response

表征安慰剂反应

• 批准号: 8419756
• 负责人: Eva Petkova
• 金额: $ 57.76万
• 依托单位: NEW YORK UNIVERSITY SCHOOL OF MEDICINE
• 依托单位国家: 美国
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-02-01 至 2017-11-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8419756
• 关键词: Acute; Architecture; Behavioral; Biological; Brain; Brain imaging; Categories; Characteristics; Chemicals; Classification; Clinical; Collection; Communities; Complex; Computing Methodologies; Controlled Clinical Trials; Coupled; Data; Data Analyses; Data Collection; Data Sources; Development; Diagnosis; Disease; Electroencephalography; Foundations; Funding; Goals; Illness impact; Image; Imaging Techniques; Individual; Instruction; Knowledge; Lead; Linear Models; Maintenance; Measurement; Measures; Medical; Medical Research; Mental disorders; Methodology; Methods; Modality; Modeling; Non-linear Models; Outcome; Patients; Pharmaceutical Preparations; Phenotype; Placebo Control; Placebo Effect; Placebos; Process; Psychotherapy; Randomized; Randomized Clinical Trials; Research; Research Personnel; Resolution; Severities; Severity of illness; Signal Transduction; Statistical Methods; Structure; Symptoms; Techniques; Technology; Testing; Therapeutic; Therapeutic Effect; United States National Institutes of Health; Work; active method; base; biosignature; clinical care; clinical phenotype; clinical practice; effective therapy; experience; falls; flexibility; meetings; new technology; novel; novel strategies; public health relevance; response; tool; treatment effect; treatment response

DESCRIPTION (provided by applicant): A major problem in developing effective treatments for mental illnesses is that specific drug effects are often obscured by the large degree of outcome variability due to placebo effects of treatment. Additionally, there is growing recognition of the therapeutic benefit of utilizing placebo effects in treating illnesses. In clinical practice prior to treatment, knowing the likelihood that a patient would benefit from nonspecific (i.e., placebo) effects can have an impact on treatment decisions. Also, knowledge of the amount of improvement during acute treatment that is due to non-specific effects would inform maintenance strategies. Consequently, the development of statistical methods that can distinguish specific and nonspecific effects of treatment will be important. Continuous advances in technology allow the development of sophisticated methodology for characterizing individuals with various psychiatric conditions; for example, brain imaging techniques provide high-resolution pictures of the structural and functional brain architecture. These complex high dimensional biological data, in conjunction with the modern development of flexible statistical methods that can accommodate such high dimensional data, presents an opportunity to obtain clinically useful characterization of patients experiencing placebo effects and to discover biosignatures for placebo response. Previously the investigators have developed methods for identifying placebo responders among drug treated subjects. Most are based on clustering and partitioning of trajectories of symptom severity during treatment. Although the developments could incorporate simple baseline covariates, the existing methodology is inadequate to deal with very high dimensional biological data such as brain images. The primary purpose of this application is to build on this foundation by developing approaches to increase the predictive power of baseline covariates that distinguish placebo response from specific response in drug treated subjects. The ultimate goal is to determine biosignatures of placebo response which we will define as patients' measures, linear combinations of such measures, or smooth, nonparametric functions of the measures, that differentially predict placebo and specific drug response. The aims are to develop models, computational methods for implementation, and analytic strategies for discovering such biosignatures, applicable to modern biomedical high-dimensional data. Our involvement in the EMBARC study (NIH-funded randomized placebo controlled clinical trial with PIs Trivedi, Weissman, McGrath, Parsey and Fava) provides access to an incredibly rich data source, consisting of extensive baseline measurements made on each subject (n=400). These data will allow for the development and testing of our methodologies for discovering biosignatures for placebo response using high dimensional data. Once developed and tested, they will be made available for research on other diseases and for different treatment modalities, such as psychotherapy. The new methods will facilitate efficient exploration of data that are typically available from standard randomized clinical trials.

描述（由申请人提供）：开发针对精神疾病的有效治疗方法的一个主要问题是，由于治疗的安慰剂效应，导致结果的很大程度的变异性常常掩盖了特定的药物作用。此外，越来越多的人认识到 利用安慰剂效应治疗疾病的治疗益处。在治疗前的临床实践中，了解患者从非特异性（即安慰剂）效应中受益的可能性可能会对治疗决策产生影响。此外，了解急性治疗期间由于非特异性效应而产生的改善程度将为维持策略提供信息。因此，开发能够区分治疗的特异性和非特异性效果的统计方法将非常重要。技术的不断进步使得能够开发出复杂的方法来描述患有各种精神疾病的个体；例如，大脑成像技术提供大脑结构和功能的高分辨率图片。这些复杂的高维生物数据，与可容纳此类高维数据的灵活统计方法的现代发展相结合，提供了获得经历安慰剂效应的患者的临床有用特征并发现安慰剂反应的生物特征的机会。 此前，研究人员已经开发出在药物治疗受试者中识别安慰剂反应者的方法。大多数是基于治疗期间症状严重程度轨迹的聚类和划分。尽管这些进展可以包含简单的基线协变量，但现有的方法不足以处理非常高维的生物数据，例如大脑图像。该应用的主要目的是在此基础上开发方法来提高基线协变量的预测能力，以区分安慰剂反应和药物治疗受试者的特定反应。最终目标是确定安慰剂反应的生物特征，我们将其定义为患者的测量值、此类测量值的线性组合或测量值的平滑非参数函数，以差异性地预测安慰剂和特定药物反应。目的是开发适用于现代生物医学高维数据的模型、实施计算方法以及发现此类生物特征的分析策略。我们参与 EMBARC 研究（NIH 资助的随机安慰剂对照临床试验，由 PI Trivedi、Weissman、McGrath、Parsey 和 Fava 参与）提供了极其丰富的数据源，其中包括对每个受试者 (n=400) 进行的广泛基线测量。这些数据将允许开发和测试我们的方法，以使用高维数据发现安慰剂反应的生物特征。一旦开发和测试完成，它们将可用于其他疾病的研究和不同的治疗方式，例如心理治疗。新方法将有助于有效探索通常可从标准随机临床试验中获得的数据。