Statistical methods for the assessment of social engagement in psychosis using digital technologies

摘要 这是琳达瓦莱里博士的K 01奖申请表，她是姆克林医院和哈佛大学的生物统计学家。 医学院瓦列里博士正在确立自己作为一个年轻的精神病生物统计学研究者的地位， 精神障碍该K 01奖项将为Valeri博士提供必要的支持，以完成 以下目标：（1）成为专注于移动的健康（mhealth）研究的精神病学生物统计学专家 （2）使用移动医疗技术对精神病患者进行调查;（3） 为移动健康研究中的先进机器学习方法开发自动化软件;以及（4）开发 独立的研究生涯。为了实现这些目标，Valeri博士组建了一个由三名成员组成的团队。 姆克林医院精神疾病科主任Dost Öngür博士领导了一个 神经影像学实验室研究精神疾病的生物学，和共同导师博士罗素舒特， 波士顿马萨诸塞州大学社会学教授，在研究 严重精神疾病患者的社会互动，以及Jukka-Pekka Onnela博士， 生物统计学在哈佛T. H。Chan公共卫生学院，他开发了一个收集原始数据的平台， 来自移动的设备的传感器数据，称为“Beiwe”，并在数字表型和 网络科学瓦列里博士的研究将集中在统计方法的发展， mhealth数据来阐明社会参与在精神病中的作用。该提案建立在 假设由被动移动的数据流（呼叫和文本日志）和移动的捕获的社交交互 调查是潜在的干预目标，可以通过促进人们对环境的感知， 社会支持和改善精神症状。在目标1（a）中，我们提出扩展机器学习 方法，贝叶斯核机器回归，用于分析移动的数据流占时间- 不同的混淆。该方法将允许在目标1（B）中建立（i）在高 社会和流动行为的被动措施与自我报告的措施， 社会互动和（ii）社会互动动态对感知社会支持和精神病的影响 在临床环境中测量的症状。此外，我们将扩展的方法，以纠正选择偏差 由于移动的调查中缺少数据而引入的问题（目标2）。对于这两个目标，Valeri博士将开发软件（目标3） 并应用这些方法来调查这些科学问题，使用的数据来自一项正在进行的研究， 姆克林医院精神障碍科，采用智能手机平台收集传感器 数据由Onnela博士开发。瓦列里博士的调查将提供初步证据的特点和 社会互动行为的时机，可以改善精神症状沿着理解， 潜在的作用机制。这项研究将构成干预研究的基础， 精神病患者的社会互动，将在未来的R 01申请中提出。 !

ABSTRACT This is an application for a K01 award for Dr. Linda Valeri, a biostatistician at McLean Hospital and the Harvard Medical School. Dr. Valeri is establishing herself as a young investigator in psychiatric biostatistics focusing on psychotic disorders. This K01 award will provide Dr. Valeri with the support necessary to accomplish the following goals: (1) to become expert in psychiatric biostatistics focusing on mobile health (mhealth) research for psychotic disorders (2) to conduct investigations using mhealth technologies in patients with psychosis; (3) to develop automated software for advanced machine learning methods in mhealth studies; and (4) to develop an independent research career. To achieve these goals, Dr. Valeri has assembled a team comprised of three mentors, Dr. Dost Öngür, Chief of the McLean Hospital Psychotic Disorders Division, who leads a neuroimaging laboratory studying the biology of psychotic illness, and co-mentors Dr. Russell Schutt, Professor of Sociology at University of Massachusetts in Boston, who has extensive experience in the study of social interactions in patients with severe mental illness, and Dr. Jukka-Pekka Onnela, Associate Professor of Biostatistics at Harvard T.H. Chan School of Public Health, who has developed a platform for collection of raw sensor data from mobile devices, called “Beiwe”, and conducts research in the fields of digital phenotyping and network science. Dr. Valeri’s research will focus on the development of statistical methods for the analysis of mhealth data to shed light on the role of social engagement in psychosis. The proposal builds upon the hypothesis that social interactions captured by passive mobile data streams (call and text logs) and mobile surveys are potential targets of intervention and could lead to a sustained recovery by promoting perceived social support and improving psychiatric symptoms. In Aim 1(a) we propose to extend a machine learning approach, Bayesian Kernel Machine Regression, for the analysis of mobile data streams accounting for time- varying confounding. The approach will allow in Aim 1(b) to establish (i) reliable links between a high dimensional time series of passive measures of social and mobility behaviors with self-reported measures of social interaction and (ii) the effect of social interaction dynamics on perceived social support and psychiatric symptoms measured in clinical settings. Further, we will extend the approach to correct for selection bias introduced by missing data in mobile surveys (Aim 2). For both aims, Dr. Valeri will develop software (Aim 3) and apply the approaches to investigate these scientific questions using data from an ongoing study based at McLean Hospital Psychotic Disorders Division that employs the smartphone platform for collection of sensor data developed by Dr. Onnela. Dr. Valeri’s investigation will provide preliminary evidence on features and timing of social interaction behaviors that can improve psychiatric symptoms along with understanding of potential mechanisms of action. This research will form the basis for an intervention study that encourages social interactions of patients with psychosis, to be proposed in a future R01 application. !