Characterizing Placebo Response

描述(申请人提供)：开发有效的精神疾病治疗方法的一个主要问题是，由于治疗的安慰剂效应，结果的很大程度变化往往掩盖了特定的药物效应。此外，越来越多的人认识到 在治疗疾病中利用安慰剂效应的治疗益处。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，了解在急性治疗期间由于非特异性影响而改善的量将为维护策略提供依据。因此，开发能够区分治疗的特定和非特定效果的统计方法将是重要的。技术的不断进步使复杂的方法得以发展，用于描述患有各种精神疾病的个人的特征；例如，脑成像技术提供了结构和功能大脑结构的高分辨率图像。这些复杂的高维生物数据，再加上可以容纳这种高维数据的灵活统计方法的现代发展，为获得经历安慰剂效应的患者的临床有用特征和发现安慰剂反应的生物特征提供了机会。此前，研究人员已经开发出在接受药物治疗的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集和划分。虽然这些发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，如脑图像。这项应用的主要目的是通过开发方法来提高基线协变量的预测能力，以区分药物治疗受试者中的安慰剂反应和特定反应，从而建立在此基础上。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，此类测量的线性组合，或测量的平滑非参数函数，以区别预测安慰剂和特定药物的反应。其目的是开发适用于现代生物医学高维数据的模型、实现的计算方法和发现此类生物签名的分析策略。我们参与了EMBARC研究(NIH资助的随机安慰剂对照临床试验，PI为Trivedi、Weissman、McGrath、Parsey和Fava)，提供了对令人难以置信的丰富数据源的访问，包括对每个受试者(n=400)进行的广泛的基线测量。这些数据将允许开发和测试我们的方法，使用高维数据发现用于安慰剂反应的生物签名。一旦开发和测试，它们将用于其他疾病的研究和不同的治疗方式，如心理治疗。新方法将促进对通常从标准随机临床试验中获得的数据的有效探索。

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.