Inferential methods for functional data from wearable devices

项目摘要/摘要 这是一个开发新的统计方法的项目，用于比较健康结果方面的受试者群体 这些数据是使用可穿戴设备的数据进行评估的。用于健康监测的廉价可穿戴传感器现在 能够产生大量纵向收集的数据，一次最多几个月。该项目将 开发能够处理此类数据的复杂性的推理方法。一个严峻的挑战是 无法测量的依赖时间的混杂因素(例如，昼夜节律和饮食模式)，进行直接比较或 除非研究是在对照的实验环境中进行的，否则跨对象借用力量是站不住脚的。 不同的选择。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 从这样的数据中获取状态。然而，这些工具不能用于协变量效应的显著fi检验，这是 例如，对于设计精确的医学干预来说是必要的，而不需要进行固有的模型选择 或者考虑到未测量的混杂因素的存在。为了克服这些不同的fi崇拜，一个系统的设计- 将开发从可穿戴设备获得的功能结果数据的推断方法。 fic的目标有三个：1)开发可穿戴设备的功能结果数据的度量标准；2)开发 活性PROLES的非参数估计和检验方法以及活性预测因子的筛选方法 ProfiLES，3)在R包中实现这些方法，并使用加速度计数据进行两个案例研究。为 目标1，该方法是将传感器数据减少到占用时间fi_LES(例如，作为活动水平的函数)， 并用生存分析和功能数据分析的方法对这些指标进行了统计建模。 消耗臭氧层物质。这将有许多好处，主要的一个是依赖时间的混杂因素变得 问题较少，因为不同受试者在时间排列上的差异的影响得到了缓解。此外, 生存分析方法可以通过将占用时间视为通过以下方式索引的事件发生时间结果 活动级别。对于目标2，将使用非参数方法来比较和排序占用时间分布 在基线协变量水平或治疗组方面特定的受试组之间进行比较。此外， 提出了一种新的基于边际筛选的标量函数回归后选择推理方法 开发的目的是识别和正式测试协变量是否与活性fifiLes显著相关。目标 3将开发R-Package实现，并将其应用于所建议的方法的试验台 两项以哥伦比亚为基础的临床研究：对纽约市Head注册儿童的体力活动研究 开始，并致力于治疗线粒体衰竭综合征的实验药物的研究。

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.