Inferential methods for functional data from wearable devices
可穿戴设备功能数据的推理方法
基本信息
- 批准号:9924432
- 负责人:
- 金额:$ 29.89万
- 依托单位:
- 依托单位国家:美国
- 项目类别:
- 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:
- 关键词:AccelerationAccelerometerBedsBypassCase StudyCharacteristicsChildClinical ResearchComputer softwareDataData AnalyticsDevelopmentDevicesDietary PracticesDrug CombinationsEnrollmentEvaluationEventGrantHead Start ProgramHealthHealth StatusHealthcareInterventionLeadMachine LearningMeasuresMethodsMitochondriaModelingMolecularMonitorMotivationNatureNew York CityObesityOccupationsOutcomeOutcome MeasurePatientsPharmacotherapyPhysical activityPhysiologicalPreschool ChildProcessProxyPublic HealthRecording of previous eventsRegimenSignal TransductionSpecific qualifier valueStatistical MethodsStatistical ModelsStochastic ProcessesSurvival AnalysisSyndromeTarget PopulationsTechniquesTestingTimeWorkanalytical methodbasecircadiandata miningdesignexperimental studyfunctional outcomesindexinginterestlower income familiesnovelpatient populationprecision medicinescreeningsensortheoriestime usetooltreatment groupwearable devicewearable sensor technology
项目摘要
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.
项目总结/文摘
项目成果
期刊论文数量(0)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
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IAN WRAY MCKEAGUE的其他文献
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{{ truncateString('IAN WRAY MCKEAGUE', 18)}}的其他基金
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 - 财政年份:2019
- 资助金额:
$ 29.89万 - 项目类别:
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:
10394221 - 财政年份:2019
- 资助金额:
$ 29.89万 - 项目类别:
Point Impact and Sparsity in Functional Data Analysis.
函数数据分析中的点影响和稀疏性。
- 批准号:
8023927 - 财政年份:2011
- 资助金额:
$ 29.89万 - 项目类别:
Point Impact and Sparsity in Functional Data Analysis.
函数数据分析中的点影响和稀疏性。
- 批准号:
8669009 - 财政年份:2011
- 资助金额:
$ 29.89万 - 项目类别:
Point Impact and Sparsity in Functional Data Analysis.
函数数据分析中的点影响和稀疏性。
- 批准号:
8505504 - 财政年份:2011
- 资助金额:
$ 29.89万 - 项目类别:
Point Impact and Sparsity in Functional Data Analysis.
函数数据分析中的点影响和稀疏性。
- 批准号:
8324206 - 财政年份:2011
- 资助金额:
$ 29.89万 - 项目类别:
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