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

摘要 这是麦克莱恩医院和哈佛大学生物统计学家琳达·瓦莱里博士的K01奖项申请表 医学院。瓦莱里博士在精神病学生物统计学领域确立了自己年轻研究员的地位，专注于 精神错乱。这一K01奖项将为瓦莱里博士提供必要的支持，以完成 以下目标：(1)成为专注于移动健康(MHealth)研究的精神病学生物统计专家 针对精神病患者(2)使用移动健康技术对精神病患者进行调查；(3) 为移动健康研究中的高级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，瓦莱里博士组建了一个由三人组成的团队 导师们，McLean医院精神障碍科主任DostÖngür博士领导着一个 研究精神疾病生物学的神经成像实验室，以及共同导师拉塞尔·舒特博士， 波士顿马萨诸塞大学社会学教授，在研究 严重精神疾病患者的社会互动，和Jukka-Pekka Onnela博士，副教授 哈佛大学公共卫生学院的生物统计学，他开发了一个收集RAW的平台 来自移动设备的传感器数据，称为“北威”，并在数字表型和数字表型和 网络科学。瓦莱里博士的研究将集中在开发统计方法来分析 移动健康数据揭示了社会参与在精神病中的作用。这项提议建立在 假设被动移动数据流(通话和文本日志)和移动设备捕获的社交交互 调查是干预的潜在目标，并可能通过促进人们对 社会支持和改善精神症状。在目标1(A)中，我们建议扩展机器学习 贝叶斯核机回归方法，用于分析占时间的移动数据流- 不同程度的混淆。该方法将允许在目标1(B)中建立(I)高 社会和流动行为的被动测量的维度时间序列和自我报告测量 社会互动和(Ii)社会互动动态对领悟社会支持和精神病学的影响 在临床环境中测量的症状。此外，我们将扩展该方法以纠正选择偏差 由移动调查中的缺失数据引入(目标2)。对于这两个目标，瓦莱里博士将开发软件(目标3) 并使用来自一项正在进行的研究的数据来应用这些方法来调查这些科学问题 McLean医院精神障碍科使用智能手机平台收集传感器 由Onnela博士开发的数据。瓦莱里博士的调查将提供有关特征和 社会互动行为的时机，可以改善精神症状以及对 潜在的作用机制。这项研究将构成一项干预研究的基础，该研究将鼓励 精神病患者的社会互动，将在未来的R01应用中提出。 好了！

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. !