Characterizing Placebo Response

表征安慰剂反应

基本信息

批准号：
8966044
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
项目类别：
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/8966044
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 Health 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 response tool treatment effect treatment response

项目摘要

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）：开发精神疾病有效治疗的一个主要问题是，由于安慰剂的治疗作用，特定的药物影响通常被大量结果变异性所掩盖。此外，人们的认可越来越在治疗疾病中利用安慰剂作用的治疗益处。在治疗前的临床实践中，知道患者将受益于非特异性（即安慰剂）作用的可能性可能会对治疗决策产生影响。同样，了解急性治疗期间的改善量，这是由于非特异性效应所致的知识，这将为维护策略提供信息。因此，可以开发可以区分特定和非特异性影响的统计方法很重要。技术的持续进步允许发展复杂的方法，以表征患有各种精神疾病的个体；例如，大脑成像技术提供了结构和功能性脑结构的高分辨率图片。这些复杂的高维生物学数据以及可以适应这种高维数据的柔性统计方法的现代发展，为获得安慰剂效应的患者的临床有用表征和发现安慰剂反应的生物签名提供了机会。以前，研究人员已经开发了鉴定药物治疗受试者中安慰剂反应者的方法。大多数基于治疗过程中症状严重程度的群体的聚类和分区。尽管这些发展可以结合简单的基线协变量，但现有的方法不足以处理非常高维生物学数据，例如脑图像。本应用的主要目的是通过开发方法来建立基础，以增加将安慰剂反应与药物治疗受试者的特定反应区分开的基线协变量的预测能力。最终目标是确定安慰剂反应的生物签名，我们将定义为患者的措施，此类度量的线性组合或措施的平滑，非参数功能，以差异地预测安慰剂和特定的药物反应。目的是开发模型，实施的计算方法以及用于发现这种生物签名的分析策略，适用于现代生物医学高维数据。我们参与了Embarc研究（NIH资助的随机安慰剂对照临床试验与PIS Trivedi，Weissman，McGrath，Parsey和Fava）提供了对非常丰富的数据源的访问，包括对每个受试者进行的广泛的基线测量（n = 400）。这些数据将允许开发和测试我们的方法，用于使用高维数据来发现安慰剂响应的生物签名。一旦开发和测试，它们将用于研究其他疾病和不同治疗方式（例如心理疗法）。这些新方法将有助于有效探索数据，这些数据通常可从标准的随机临床试验中获得。