Characterizing Placebo Response

表征安慰剂反应

• 批准号: 9173467
• 负责人: Eva Petkova
• 金额: $ 48.13万
• 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/9173467
• 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 Analytics; 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; Modernization; 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; Treatment outcome; United States National Institutes of Health; Work; active method; base; biosignature; clinical care; clinical phenotype; clinical practice; effective therapy; experience; falls; flexibility; high dimensionality; new technology; novel; novel strategies; predictive of treatment response; public health relevance; response; tool; treatment effect

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）：在开发有效的精神疾病治疗方法时的一个主要问题是，由于治疗的安慰剂效应，特定的药物效应往往被结果的巨大差异所掩盖。此外，人们越来越认识到， 利用安慰剂效应治疗疾病的治疗效果。在治疗前的临床实践中，了解患者从非特异性（即，安慰剂）效应可对治疗决定产生影响。此外，了解急性治疗期间由于非特异性效应而导致的改善程度将为维持治疗策略提供信息。因此，发展能够区分特异性和非特异性治疗效果的统计方法将是重要的。技术的不断进步使人们能够开发出复杂的方法来表征患有各种精神疾病的个体;例如，脑成像技术提供了结构和功能脑结构的高分辨率图片。这些复杂的高维生物学数据，结合现代发展的灵活的统计方法，可以容纳这样的高维数据，提供了一个机会，以获得临床上有用的表征患者经历安慰剂效应，并发现安慰剂反应的生物签名。 以前，研究人员已经开发出了在药物治疗受试者中识别安慰剂应答者的方法。大多数是基于治疗期间症状严重程度轨迹的聚类和分区。虽然发展可以纳入简单的基线协变量，现有的方法是不足以处理非常高维的生物数据，如脑图像。本申请的主要目的是在此基础上，通过开发方法来增加基线协变量的预测能力，以区分药物治疗受试者的安慰剂应答与特异性应答。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、这些测量值的线性组合或测量值的平滑非参数函数，这些测量值差异性地预测安慰剂和特定药物反应。其目的是开发模型，计算方法的实施，和分析策略，发现这样的生物签名，适用于现代生物医学高维数据。我们参与EMBARC研究（NIH资助的随机安慰剂对照临床试验，PI Trivedi，Weissman，麦格拉思，Parsey和Fava）提供了一个非常丰富的数据源，包括对每个受试者进行的广泛基线测量（n=400）。这些数据将允许开发和测试我们的方法，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，它们将可用于其他疾病的研究和不同的治疗方式，如心理治疗。新方法将有助于有效地探索通常从标准随机临床试验中获得的数据。