Statistical Methods for Developing RDoC-based Multidimensional Profiles

Meredith Lotz Wallace博士的长期研究目标是成为一名精神病学生物统计学家，开发和应用新方法来帮助研究人员实现NIMH研究领域标准（RDoC）项目的目标。RDoC项目表明，精神障碍的理想特征可能是多方面的体征和症状（例如，脑电路、生理学、行为和自我报告）;然而，这种新的范例提出了实质性的挑战，因为大多数现有的统计方法没有被设计成适应所产生的数据类型的混合。华莱士博士提出的指导研究科学家发展（K 01）奖是她实现长期研究目标的第一步;它将提供她开发统计方法所需的培训和支持，使RDoC项目成为一个更可行的研究框架。Wallace博士的K 01的总体目标是开发和应用新的聚类方法，将多种类型的密集测量数据结合起来。在目标1中，Wallace博士将开发聚类方法，该方法将通过多导睡眠图、活动记录仪和每日自我报告措施捕获的密集测量数据结合起来。这些方法将在两个不同的理论框架下开发，以增强其对K 01中具体解决的应用程序的通用性。在目标2中，华莱士博士将把她的方法应用于一个数据库，该数据库包含来自1000多个个体的多个维度的观察结果，这些个体具有与损失、焦虑和睡眠/觉醒调节相关的各种水平的干扰。该方法的应用将揭示多维配置文件，可能会切断目前的DSM为基础的诊断。这些配置文件的研究将澄清在何种程度上的多个层面的体征和症状在于一个真正的连续体或分离成离散类。虽然候选人在生物统计学方面的背景使她开始这项研究，但其成功完成最终取决于三个领域的额外培训：1）聚类和密集测量数据方法的数学基础，2）负价系统（例如，损失和焦虑），和3）睡眠/觉醒调节。这种培训将通过教程，定向阅读，正式课程，动手临床工作和专业会议来实现。为了实现她的培训目标，Wallace博士组建了一支由临床和方法学导师和顾问组成的强大团队。临床指导团队由国际公认的情绪相关疾病专家Ellen Frank博士领导。该方法指导团队由Satish Iyengar博士领导，他是精神病学统计领域的领导者，擅长对密集测量数据进行聚类和建模。该项目的培训和研究将为Wallace博士提供她在K 01的第四年提交R 01所需的经验和技能。该R 01将允许Wallace博士扩展她提出的方法，并将其应用于基于其他维度和研究领域开发基于RDoC的多维配置文件。

DESCRIPTION (provided by applicant): Dr. Meredith Lotz Wallace's long-term research goal is to become a psychiatric biostatistician who develops and applies novel methods to aid researchers in attaining the goals of the NIMH Research Domain Criteria (RDoC) project. The RDoC project suggests that mental disorders may be ideally characterized by multiple dimensions of signs and symptoms (e.g., brain circuitry, physiology, behavior, and self-report); however, this new paradigm poses substantial challenges because most existing statistical methods were not designed to accommodate the resulting mixture of data types. Dr. Wallace's proposed Mentored Research Scientist Development (K01) award is the first step towards her long-term research goal; it will provide the training and support she needs to develop statistical methods that will make the RDoC project a more viable research framework. The overall aim of Dr. Wallace's K01 is to develop and apply novel clustering methods that incorporate multiple types of intensively measured data. In Aim 1, Dr. Wallace will develop clustering methods that incorporate intensively measured data captured through polysomnography, actigraphy, and daily self-report measures. These methods will be developed under two different theoretical frameworks to enhance their generalizability to applications beyond those addressed specifically in the K01. In Aim 2, Dr. Wallace will apply her methods to a data base containing multiple dimensions of observations from over 1000 individuals with a variety of levels of disturbances related to loss, anxiety, and sleep/wake regulation. The methods' application will reveal multidimensional profiles that may cut across the current DSM-based diagnoses. A study of these profiles will clarify the extent to which the multiple dimensions of signs and symptoms lie on a true continuum or separate into discrete classes. Although the candidate's background in biostatistics has primed her to begin this research, its successful completion ultimately hinges on additional training in three areas: 1) mathematical foundations of methods for clustering and intensively measured data, 2) negative valence systems (e.g., loss and anxiety), and 3) sleep/wake regulation. This training will be achieved through tutorials, directed readings, formal coursework, hands-on clinical work, and professional conferences. To achieve her training goals, Dr. Wallace has assembled a strong team of clinical and methodological mentors and consultants. The clinical mentoring team is led by Dr. Ellen Frank, an internationally recognized expert in mood-related disorders. The methodological mentoring team is led by Dr. Satish Iyengar, a leader the field of psychiatric statistics with expertise in clustering and modeling intensively measured data. The training and research resulting from this project will provide Dr. Wallace with the experience and skills she needs to submit an R01 in the fourth year of the K01. This R01 will allow Dr. Wallace to expand on her proposed methods and apply them to develop RDoC-based multidimensional profiles based on additional dimensions and domains of study.