Inferential methods for functional data from wearable devices

可穿戴设备功能数据的推理方法

• 批准号: 9924432
• 负责人: IAN WRAY MCKEAGUE
• 金额: $ 29.89万
• 依托单位: COLUMBIA UNIVERSITY HEALTH SCIENCES
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-04-15 至 2024-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9924432
• 关键词: Acceleration; Accelerometer; Beds; Bypass; Case Study; Characteristics; Child; Clinical Research; Computer software; Data; Data Analytics; Development; Devices; Dietary Practices; Drug Combinations; Enrollment; Evaluation; Event; Grant; Head Start Program; Health; Health Status; Healthcare; Intervention; Lead; Machine Learning; Measures; Methods; Mitochondria; Modeling; Molecular; Monitor; Motivation; Nature; New York City; Obesity; Occupations; Outcome; Outcome Measure; Patients; Pharmacotherapy; Physical activity; Physiological; Preschool Child; Process; Proxy; Public Health; Recording of previous events; Regimen; Signal Transduction; Specific qualifier value; Statistical Methods; Statistical Models; Stochastic Processes; Survival Analysis; Syndrome; Target Populations; Techniques; Testing; Time; Work; analytical method; base; circadian; data mining; design; experimental study; functional outcomes; indexing; interest; lower income families; novel; patient population; precision medicine; screening; sensor; theories; time use; tool; treatment group; wearable device; wearable sensor technology

Project Summary/Abstract This is a project to develop new statistical methods for comparing groups of subjects in terms of health outcomes that are assessed using data from wearable devices. Inexpensive wearable sensors for health monitoring are now capable of generating massive amounts of data collected longitudinally, up to months at a time. The project will develop inferential methods that can deal with the complexity of such data. A serious challenge is the presence of unmeasured time-dependent confounders (e.g., circadian and dietary patterns), making direct comparisons or borrowing strength across subjects untenable unless the studies are carried out in controlled experimental con- ditions. Generic data mining and machine learning tools have been widely used to provide predictions of health status from such data. However, such tools cannot be used for significance testing of covariate effects, which is necessary for designing precision medicine interventions, for example, without taking the inherent model selection or the presence of the unmeasured confounders into account. To overcome these difficulties, a systematic de- velopment of inferential methods for functional outcome data obtained from wearable devices will be carried out. There are three specific aims: 1) Develop metrics for functional outcome data from wearable devices, 2) Develop nonparametric estimation and testing methods for activity profiles and a screening method for predictors of activity profiles, 3) Implement the methods in an R package and carry out two case studies using accelerometer data. For Aim 1, the approach is to reduce the sensor data to occupation time profiles (e.g., as a function of activity level), and formulate the statistical modeling in terms of these profiles using survival and functional data analytic meth- ods. This will have a number of advantages, the principal one being that time-dependent confounders become less problematic because the effect of differences in temporal alignment across subjects is mitigated. In addition, survival analysis methods can be applied by viewing the occupation time as a time-to-event outcome indexed by activity level. For Aim 2, nonparametric methods will be used to compare and order occupation time distributions between groups of subjects that are specified in terms of baseline covariate levels or treatment groups. Further, a new method of post-selection inference based on marginal screening for function-on-scalar regression will be developed to identify and formally test whether covariates are significantly associated with activity profiles. Aim 3 will develop an R-package implementation, and as a test-bed for the proposed methods they will be applied to two Columbia-based clinical studies: to the study of physical activity in children enrolled in New York City Head Start, and to the study of experimental drugs for the treatment of mitochondrial depletion syndrome.

项目摘要/摘要 这是一个项目，旨在开发新的统计方法，以比较健康结果的主题组 使用可穿戴设备的数据进行评估。现在正在使用廉价的可穿戴传感器来进行健康监测 能够一次纵向收集的大量数据，一次最多几个月。该项目将 开发可以处理此类数据复杂性的推论方法。一个严重的挑战是存在 未衡量的时间依赖性混杂因素（例如昼夜节律和饮食模式），进行直接比较或 除非在受控的实验概念中进行研究 差异。通用数据挖掘和机器学习工具已被广泛用于提供健康的预测 从这些数据中的状态。但是，这样的工具不能用于对协变量效应的显着测试， 例如，设计精确医学干预措施所必需的，而无需进行固有模型选择 或者考虑到未衡量的混杂因素的存在。为了克服这些困难，系统性 将执行从可穿戴设备获得的功能结果数据的推论方法的速度。 有三个特定的目的：1）开发可穿戴设备功能结果数据的指标，2）开发 活性纤维的非参数估计和测试方法和活动预测指标的筛选方法 pro文件，3）在R软件包中实现方法，并使用加速度计数据进行两个案例研究。为了 AIM 1，方法是减少传感器数据以占据时间文件（例如，作为活动水平的函数）， 并使用生存和功能数据分析元来以这些专题方式来制定统计建模 ODS。这将具有许多优势，主要的是时间依赖的混杂因素 较少的问题是因为减轻跨受试者临时排列的差异的影响。此外， 生存分析方法可以通过将占用时间视为索引的事件结果来应用 活动水平。对于AIM 2，非参数方法将用于比较和订购可用性时间分布 在基线协变量或治疗组方面指定的受试者组之间。更远， 基于边际筛选在降低功能回归的边际筛选的新的选择后推断方法将是 开发的旨在识别和正式测试协变量是否与活性填充物显着相关。目的 3将开发R包装实现，作为针对所提出的方法的测试床，它们将被应用于 两项基于哥伦比亚的临床研究：研究纽约市长的儿童体育锻炼 开始，并研究实验药物以治疗线粒体耗竭综合征。