자료유형  

 학위논문

Control Number  

0016933115

International Standard Book Number  

9798379615901

Dewey Decimal Classification Number  

574

Main Entry-Personal Name  

Li, Shanpeng.

Publication, Distribution, etc. (Imprint  

[S.l.] : University of California, Los Angeles., 2023

Publication, Distribution, etc. (Imprint  

Ann Arbor : ProQuest Dissertations & Theses, 2023

Physical Description  

1 online resource(122 p.)

General Note  

Source: Dissertations Abstracts International, Volume: 84-12, Section: B.

General Note  

Advisor: Li, Gang.

Dissertation Note  

Thesis (Ph.D.)--University of California, Los Angeles, 2023.

Restrictions on Access Note  

This item must not be sold to any third party vendors.

Summary, Etc.  

요약It is often of interest to study the temporal patterns of longitudinal biomarker(s) that are potentially correlated and predictive of time-to-event outcomes in biomedical studies. In this dissertation, I develop new approaches for joint modeling of longitudinal and time-toevent data. My dissertation consists of three projects. In Chapter 2, I develop customized linear scan algorithms to speed up the computation of semi-parametric joint models by linearizing the computational burden of the estimation procedure from O(n2 ) or O(n3 ) to O(n). Compared to the existing software and packages on semi-parametric joint models, our implementations can provide more than thousands of speed-ups when the sample size goes large. In Chapter 3, motivated by the Multi-Ethnic Study of Atherosclerosis (MESA), I propose a novel joint model to account for the heterogeneity of within-subject variability of a longitudinal outcome and demonstrate that it improves the dynamic prediction accuracy of predicting the future event probabilities of both heart failure and death across MESA individuals. In Chapter 4, I extend the joint model described in Chapter 3 to handle interval-censored covariates as missing data due to the unknown initial event time. Using age at diagnosis of diabetes as an interval-censored covariate, we revisit the UK-Biobank data to illustrate that our proposed joint model can yield clinically meaningful parameter estimates, compared to the existing methods such as midpoint imputation, which can lead to problematic conclusion on the effect of covariates on the outcomes. 

Subject Added Entry-Topical Term  

Biostatistics.

Subject Added Entry-Topical Term  

Endocrinology.

Index Term-Uncontrolled  

Biomedical studies

Index Term-Uncontrolled  

Temporal patterns

Index Term-Uncontrolled  

Joint modeling

Index Term-Uncontrolled  

Longitudinal

Added Entry-Corporate Name  

University of California, Los Angeles Biostatistics 0132

Host Item Entry  

Dissertations Abstracts International. 84-12B.

Host Item Entry  

Dissertation Abstract International

Electronic Location and Access  

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Control Number  

joongbu:642214