Sleep Health Profiles and Mortality Risk in Older Adults: A Multi-Cohort Application of Novel Statistical Methods
Sleep Health Profiles and Mortality Risk in Older Adults: A Multi-Cohort Application of Novel Statistical Methods
批准号:
9360318
负责人:
MEREDITH JOANNE LOTZ WALLACE
金额:
$35.33万
依托单位国家:
美国
项目类别:
财政年份:
2017
资助国家:
美国
项目状态:
已结题
起止时间:
2017-08-01 至 2020-05-31
关键词:
AdultAgeAgingAlgorithmsAmericasAsiaAttentionAwarenessBedsCardiac healthCardiovascular DiseasesCharacteristicsCognitionCohort StudiesCox Proportional Hazards ModelsDataData AnalysesData SourcesDevelopmentDimensionsDrowsinessElderlyEnsureEpidemiologyGenderGoalsHealthIndividualInterventionMachine LearningMapsMental DepressionMethodologyMethodsModelingMorbidity - disease rateOutcomeParentsPathway interactionsPatient Self-ReportPatternPolysomnographyPopulationProtocols documentationPublic HealthRaceReproducibilityResearchResearch PersonnelRiskRisk FactorsSample SizeSamplingSleepSleep Apnea SyndromesSleep DisordersSmokingStatistical MethodsStatistical ModelsStructureSubgroupSurvival AnalysisTechniquesTestingTimeTreesVisionWakefulnessWell in selfWisconsinWomanactigraphyagedbasecardiovascular healthcohortdiariesfallsflexibilityfollow-uphealth practiceimprovedlensmenmortalitynonlinear regressionnovelosteoporosis with pathological fractureprediction algorithmsatisfactionscreeningsextherapy developmenttool
中文摘要
项目摘要/摘要
英文摘要
PROJECT SUMMARY / ABSTRACT
Individual sleep disorders and characteristics are known to be associated with adverse health outcomes.
However, few studies consider sleep in a multivariate context. Therefore, we propose a paradigmatic shift in
sleep research towards sleep health. Conducting research through a sleep health lens can change health
practice by clarifying which sleep characteristics are most important to treat, enhancing screening efforts, and
facilitating the generalization of findings to all individuals, not just those with specific sleep disorders or
complaints. Before achieving these long-term goals, however, the crucial first steps in the study of sleep health
are to determine which sleep profiles predict health outcomes (Aim 1), and develop predictive algorithms that
can identify individuals at risk of adverse health outcomes based on their sleep and other risk factors (Aim 2).
These aims can only be achieved with an awareness of the statistical challenges related to the inherent
multidimensionality of sleep health (e.g. multiple domains of sleep, multiple characteristics to represent each
domain, multiple data sources, etc.). This multidimensionality generates substantial methodological barriers
regarding variable and model selection and threatens researchers' abilities to develop transparent and
reproducible models. In this R01, investigators will apply rigorous, sophisticated statistical methods to a large
aggregated sample of adults aged >60 with self-report and polysomnographic characterization and over 10
years of longitudinal follow-up from three parent studies: Osteoporotic Fractures in Men Sleep Study (MrOS),
Study of Osteoporotic Fractures (SOF), and the Sleep Heart Health Study (SHHS). In Aim 1, investigators will
determine which multidimensional sleep profiles predict time to mortality in older adults using Cox-proportional
hazards models, tree-structured survival analysis, and clustering. In Aim 2, investigators will develop powerful
machine learning algorithms that incorporate sleep and other risk factors to identify which older adults have the
greatest risk for early mortality. Within each aim, the impact of sex and race will be rigorously investigated,
and models will be internally validated in independent samples to ensure reproducibility. The Secondary Aim is
to investigate generalizability to studies with different demographic profiles and sleep protocols (Wisconsin
Sleep Cohort Study, Honolulu Asia Aging Study of Sleep America) and different data types (sleep diary,
actigraphy). All models will be developed in the context of other important known risk factors, including but not
limited to age, gender, race, sleep apnea, cardiovascular disease, smoking, and BMI. Findings from Aim 1 will
jumpstart subsequent mechanistic research focused on sleep-related causal factors of mortality, leading to
identification of targeted treatments for sleep problems that could reduce risk of mortality and morbidity.
Findings from Aim 2 will provide important preliminary evidence for enhanced screening tools that can more
accurately identify which individuals are at risk for adverse health outcomes.
期刊论文(0)
专著(0)
科研奖励(0)
会议论文
Data Management and Statistics Core
-
批准号:10442460
-
项目类别:
-
资助金额:$29.92万
-
财政年份:2020
-
负责人:MEREDITH JOANNE LOTZ WALLACE
-
依托单位:
Data Management and Statistics Core
-
批准号:10217069
-
项目类别:
-
资助金额:$29.46万
-
财政年份:2020
-
负责人:MEREDITH JOANNE LOTZ WALLACE
-
依托单位:
Data Management and Statistics Core
-
批准号:10655432
-
项目类别:
-
资助金额:$29.92万
-
财政年份:2020
-
负责人:MEREDITH JOANNE LOTZ WALLACE
-
依托单位:
Sleep Health Profiles Predicting Impaired Cognition and Depressive Symptoms in Older Adults: Extending Novel Statistical Methods in Multi-Cohort Applications
-
批准号:10209375
-
项目类别:
-
资助金额:$196.81万
-
财政年份:2017
-
负责人:MEREDITH JOANNE LOTZ WALLACE
-
依托单位:
Statistical Methods for Developing RDoC-based Multidimensional Profiles
-
批准号:8507978
-
项目类别:
-
资助金额:$11.87万
-
财政年份:2013
-
负责人:MEREDITH JOANNE LOTZ WALLACE
-
依托单位:
Statistical Methods for Developing RDoC-based Multidimensional Profiles
-
批准号:8643291
-
项目类别:
-
资助金额:$11.79万
-
财政年份:2013
-
负责人:MEREDITH JOANNE LOTZ WALLACE
-
依托单位:
Data Management and Statistics Core
-
批准号:10022614
-
项目类别:
-
资助金额:$29.46万
-
财政年份:--
-
负责人:MEREDITH JOANNE LOTZ WALLACE
-
依托单位:
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