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: 10605202
• 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 2025-03-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10605202
• 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 Assessment; 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; circadian; data complexity; data mining; design; experimental study; functional outcomes; health assessment; 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.

项目概要/摘要 该项目旨在开发新的统计方法，用于比较受试者组的健康结果 使用可穿戴设备的数据进行评估。用于健康监测的廉价可穿戴传感器现已上市 能够生成纵向收集的大量数据，一次长达数月。该项目将 开发可以处理此类数据复杂性的推理方法。一个严峻的挑战是存在 未测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 除非研究是在受控实验环境中进行的，否则跨学科借用力量是站不住脚的。 的版本。通用数据挖掘和机器学习工具已被广泛用于提供健康预测 此类数据的状态。然而，此类工具不能用于协变量效应的显着性检验，即 例如，设计精准医学干预措施所必需的，而不采用固有的模型选择 或考虑到未测量的混杂因素的存在。为了克服这些困难，需要采取系统化的措施 将开发从可穿戴设备获得的功能结果数据的推理方法。 有三个具体目标：1）开发可穿戴设备功能结果数据的指标，2）开发 活动概况的非参数估计和测试方法以及活动预测因子的筛选方法 配置文件，3) 在 R 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 目标 1，该方法是将传感器数据简化为占用时间曲线（例如，作为活动水平的函数）， 并使用生存和功能数据分析方法根据这些概况制定统计模型 OD。这将有很多优点，最主要的一个是时间相关的混杂因素变得 问题较少，因为受试者之间时间对齐差异的影响得到了缓解。此外， 可以通过将占用时间视为事件结果的时间来应用生存分析方法，索引为 活动水平。对于目标 2，将使用非参数方法对占用时间分布进行比较和排序 在基线协变量水平或治疗组中指定的受试者组之间。更远， 一种基于标量函数回归边缘筛选的新选择后推理方法 开发用于识别并正式测试协变量是否与活动概况显着相关。目的 3 将开发一个 R 包实现，并作为它们将应用于的建议方法的测试平台 两项基于哥伦比亚的临床研究：对纽约市注册儿童身体活动的研究 开始，并致力于研究治疗线粒体耗竭综合征的实验药物。