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Accounting for Heterogeneity in Large-Scale Observational Studies
Accounting for Heterogeneity in Large-Scale Observational Studies
상세정보
- 자료유형
- 학위논문 서양
- 최종처리일시
- 20260202104711
- ISBN
- 9798290909875
- DDC
- 574
- 저자명
- Aron, Jordan.
- 서명/저자
- Accounting for Heterogeneity in Large-Scale Observational Studies
- 발행사항
- [Sl] : University of Minnesota, 2025
- 발행사항
- Ann Arbor : ProQuest Dissertations & Theses, 2025
- 형태사항
- 110 p
- 주기사항
- Source: Dissertations Abstracts International, Volume: 87-01, Section: B.
- 주기사항
- Advisor: Fiecas, Mark;Albert, Paul.
- 학위논문주기
- Thesis (Ph.D.)--University of Minnesota, 2025.
- 초록/해제
- 요약Large-scale observational studies that collect data from wearable devices provide a unique opportunity to analyze population level data on an individual scale. There is significant heterogeneity both within individuals (intra-individual) and between individuals (inter-individual). Intra-individual heterogeneity refers to variability due to personal day-to-day changes. Inter-individual heterogeneity focuses on the differences between people, possibly due to sociodemographic or behavioral factors. It is essential to use statistical models that address these complexities. We focus on the sleep-wake cycle and physical activity, which have been shown to be important determinants of mortality. Using data from the National Health and Nutrition Examination Survey (NHANES), we leverage quantitative physical activity and light data from wearable physical activity monitors to accurately quantify the sleep-wake cycle and physical activity. We propose a novel joint latent class model (JLCM) that combines a mixture of hidden Markov models for the longitudinal physical activity and light data with a Cox proportional hazards model for survival outcomes. From our models, we identify distinct behavioral profiles associated with mortality risk. Reduced wake physical activity and increased sleep physical activity are both linked to increased mortality. Furthermore, we demonstrate that compared to our JLCM, conventional two-stage modeling approaches underestimate these effects. Our JLCM provides a robust framework for uncovering latent behavioral classes that contribute to mortality risk.
- 일반주제명
- Biostatistics
- 일반주제명
- Statistics
- 일반주제명
- Bioinformatics
- 키워드
- Air pollution
- 키워드
- Heterogeneity
- 키워드
- Joint models
- 기타저자
- University of Minnesota Biostatistics
- 기본자료저록
- Dissertations Abstracts International. 87-01B.
- 전자적 위치 및 접속
- 로그인 후 원문을 볼 수 있습니다.
MARC
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■006m o d
■007cr#unu||||||||
■020 ▼a9798290909875
■035 ▼a(MiAaPQ)AAI32118805
■040 ▼aMiAaPQ▼cMiAaPQ
■0820 ▼a574
■1001 ▼aAron, Jordan.
■24510▼aAccounting for Heterogeneity in Large-Scale Observational Studies
■260 ▼a[Sl]▼bUniversity of Minnesota▼c2025
■260 1▼aAnn Arbor▼bProQuest Dissertations & Theses▼c2025
■300 ▼a110 p
■500 ▼aSource: Dissertations Abstracts International, Volume: 87-01, Section: B.
■500 ▼aAdvisor: Fiecas, Mark;Albert, Paul.
■5021 ▼aThesis (Ph.D.)--University of Minnesota, 2025.
■520 ▼aLarge-scale observational studies that collect data from wearable devices provide a unique opportunity to analyze population level data on an individual scale. There is significant heterogeneity both within individuals (intra-individual) and between individuals (inter-individual). Intra-individual heterogeneity refers to variability due to personal day-to-day changes. Inter-individual heterogeneity focuses on the differences between people, possibly due to sociodemographic or behavioral factors. It is essential to use statistical models that address these complexities. We focus on the sleep-wake cycle and physical activity, which have been shown to be important determinants of mortality. Using data from the National Health and Nutrition Examination Survey (NHANES), we leverage quantitative physical activity and light data from wearable physical activity monitors to accurately quantify the sleep-wake cycle and physical activity. We propose a novel joint latent class model (JLCM) that combines a mixture of hidden Markov models for the longitudinal physical activity and light data with a Cox proportional hazards model for survival outcomes. From our models, we identify distinct behavioral profiles associated with mortality risk. Reduced wake physical activity and increased sleep physical activity are both linked to increased mortality. Furthermore, we demonstrate that compared to our JLCM, conventional two-stage modeling approaches underestimate these effects. Our JLCM provides a robust framework for uncovering latent behavioral classes that contribute to mortality risk.
■590 ▼aSchool code: 0130.
■650 4▼aBiostatistics
■650 4▼aStatistics
■650 4▼aBioinformatics
■653 ▼aAir pollution
■653 ▼aHeterogeneity
■653 ▼aHidden Markov models
■653 ▼aJoint models
■653 ▼aObservational studies
■653 ▼aSurvival analysis
■690 ▼a0308
■690 ▼a0715
■690 ▼a0463
■71020▼aUniversity of Minnesota▼bBiostatistics.
■7730 ▼tDissertations Abstracts International▼g87-01B.
■790 ▼a0130
■791 ▼aPh.D.
■792 ▼a2025
■793 ▼aEnglish
■85640▼uhttp://www.riss.kr/pdu/ddodLink.do?id=T17358506▼nKERIS▼z이 자료의 원문은 한국교육학술정보원에서 제공합니다.
