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Variance Components, Correlation Components, Canonical Correlation, and Prediction in Mixed Models
Variance Components, Correlation Components, Canonical Correlation, and Prediction in Mixed Models
상세정보
- 자료유형
- 학위논문 서양
- 최종처리일시
- 20260202102954
- ISBN
- 9798315714255
- DDC
- 574
- 서명/저자
- Variance Components, Correlation Components, Canonical Correlation, and Prediction in Mixed Models
- 발행사항
- [Sl] : The University of North Carolina at Chapel Hill, 2025
- 발행사항
- Ann Arbor : ProQuest Dissertations & Theses, 2025
- 형태사항
- 95 p
- 주기사항
- Source: Dissertations Abstracts International, Volume: 86-11, Section: B.
- 주기사항
- Advisor: Qaqish, Bahjat.
- 학위논문주기
- Thesis (Dr.P.H.)--The University of North Carolina at Chapel Hill, 2025.
- 초록/해제
- 요약Analyzing longitudinal outcomes presents distinct challenges due to within- and between-subject variation, manifesting as within-subject correlation. Linear mixed models (LMMs) and canonical correlation analysis (CCA) offer valuable insights into the relationships within such data structures. LMMs are fundamental for accommodating both fixed and random effects in longitudinal data analysis. CCA, traditionally applied to independent observations, explores relationships between two sets of variables via linear combinations. Combining these methodologies enhances understanding of both within-subject and between-subject associations in longitudinal data.Chapter 2 extends the concept of the intraclass correlation coefficient (ICC) to a broad class of linear and generalized linear mixed models. This extension introduces a variance fraction index that differs from pairwise correlations. A general formula links pairwise correlation and variance fraction indices, applicable to all mixed models, including those addressing overdispersion. These indices are demonstrated in linear, logistic, and loglinear mixed models, with graphical evaluations using real data.Chapter 3 investigates the estimation and inference of canonical correlation in the context of linear mixed models. Canonical correlation is used to interpret and summarize estimates of variance components, addressing issues such as temporal misalignment and missing values in longitudinal data. The canonical correlation parameter is estimated using variance-component estimates from linear mixed models. CCA is also examined as a tool for interpreting variance components and assessing covariate assumptions.Chapter 4 explores relationships between the random effects structure of linear mixed models and intracluster correlations frequently used to design and power cluster randomized trials. Methods to generate marginal pairwise correlations for mixed models, including continuous, binary, and count outcomes, are described. These pairwise correlations link mixed and marginal models and are functions of variance fractions explained by cluster factors. The approach is illustrated with a stepped wedge cluster randomized trial for antibiotic stewardship, analyzed using generalized estimating equations.
- 일반주제명
- Biostatistics
- 일반주제명
- Applied mathematics
- 기타저자
- The University of North Carolina at Chapel Hill Biostatistics
- 기본자료저록
- Dissertations Abstracts International. 86-11B.
- 전자적 위치 및 접속
- 로그인 후 원문을 볼 수 있습니다.
MARC
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■020 ▼a9798315714255
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■040 ▼aMiAaPQ▼cMiAaPQ
■0820 ▼a574
■1001 ▼aMcBride, Ryan Jameson.
■24510▼aVariance Components, Correlation Components, Canonical Correlation, and Prediction in Mixed Models
■260 ▼a[Sl]▼bThe University of North Carolina at Chapel Hill▼c2025
■260 1▼aAnn Arbor▼bProQuest Dissertations & Theses▼c2025
■300 ▼a95 p
■500 ▼aSource: Dissertations Abstracts International, Volume: 86-11, Section: B.
■500 ▼aAdvisor: Qaqish, Bahjat.
■5021 ▼aThesis (Dr.P.H.)--The University of North Carolina at Chapel Hill, 2025.
■520 ▼aAnalyzing longitudinal outcomes presents distinct challenges due to within- and between-subject variation, manifesting as within-subject correlation. Linear mixed models (LMMs) and canonical correlation analysis (CCA) offer valuable insights into the relationships within such data structures. LMMs are fundamental for accommodating both fixed and random effects in longitudinal data analysis. CCA, traditionally applied to independent observations, explores relationships between two sets of variables via linear combinations. Combining these methodologies enhances understanding of both within-subject and between-subject associations in longitudinal data.Chapter 2 extends the concept of the intraclass correlation coefficient (ICC) to a broad class of linear and generalized linear mixed models. This extension introduces a variance fraction index that differs from pairwise correlations. A general formula links pairwise correlation and variance fraction indices, applicable to all mixed models, including those addressing overdispersion. These indices are demonstrated in linear, logistic, and loglinear mixed models, with graphical evaluations using real data.Chapter 3 investigates the estimation and inference of canonical correlation in the context of linear mixed models. Canonical correlation is used to interpret and summarize estimates of variance components, addressing issues such as temporal misalignment and missing values in longitudinal data. The canonical correlation parameter is estimated using variance-component estimates from linear mixed models. CCA is also examined as a tool for interpreting variance components and assessing covariate assumptions.Chapter 4 explores relationships between the random effects structure of linear mixed models and intracluster correlations frequently used to design and power cluster randomized trials. Methods to generate marginal pairwise correlations for mixed models, including continuous, binary, and count outcomes, are described. These pairwise correlations link mixed and marginal models and are functions of variance fractions explained by cluster factors. The approach is illustrated with a stepped wedge cluster randomized trial for antibiotic stewardship, analyzed using generalized estimating equations.
■590 ▼aSchool code: 0153.
■650 4▼aBiostatistics
■650 4▼aApplied mathematics
■653 ▼aGeneralized mixed models
■653 ▼aIntraclass correlation
■653 ▼aVariance components
■690 ▼a0308
■690 ▼a0796
■690 ▼a0364
■71020▼aThe University of North Carolina at Chapel Hill▼bBiostatistics.
■7730 ▼tDissertations Abstracts International▼g86-11B.
■790 ▼a0153
■791 ▼aDr.P.H.
■792 ▼a2025
■793 ▼aEnglish
■85640▼uhttp://www.riss.kr/pdu/ddodLink.do?id=T17356570▼nKERIS▼z이 자료의 원문은 한국교육학술정보원에서 제공합니다.
