자료유형  

 학위논문

Control Number  

0016934268

International Standard Book Number  

9798380120524

Dewey Decimal Classification Number  

574

Main Entry-Personal Name  

Ji, Ziyu.

Publication, Distribution, etc. (Imprint  

[S.l.] : University of Minnesota., 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: 85-02, Section: B.

General Note  

Advisor: Wolfson, Julian.

Dissertation Note  

Thesis (Ph.D.)--University of Minnesota, 2023.

Restrictions on Access Note  

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

Summary, Etc.  

요약The rapid advancement of big data in various fields of healthcare has sparked a growing interest in making inferences about source-specific parameters. While technologies have provided a wealth of data for each person, the optimal utilization of multisource population data to enhance source-specific inference remains an open research question. Numerous existing methods have been developed to borrow information from multiple supplemental sources, such as individuals, in order to support individualized parameter inference in a primary source. Multisource exchangeability models (MEM) have been introduced as such a dynamic data borrowing approach, determining the amount of information to borrow based on the exchangeability between the primary and supplemental sources, where exchangeability is defined as the equality of a target parameter. Motivated by MEM, this thesis proposes three innovative Bayesian statistical databorrowing methods to leverage complicated types of information. The first method is the data-driven MEM (dMEM), a two-stage approach that incorporates source selection and clustering to enable the inclusion of an arbitrary number of sources in individualized inference while ensuring computational efficiency and data effectiveness. The second method is the generalized Reinforced Borrowing Framework (RBF), which utilizes a distance-embedded prior leveraging not only data related to the target parameter but also different types of auxiliary information sources, to reinforce inference on the target parameter. The third method is the Synthetic Conditional Borrowing (SCB) approach, which generates synthetic sources from predictive models based on a range of covariates specific to the primary source. This method facilitates the inference of target parameters by incorporating a novel definition of conditional exchangeability that is not limited to strict homogeneity between sources but also considers the alignment in covariates. All three methods demonstrate robustness in various simulation settings and outperform previously proposed methods in both simulated and real-world scenarios.

Subject Added Entry-Topical Term  

Biostatistics.

Subject Added Entry-Topical Term  

Bioinformatics.

Index Term-Uncontrolled  

Bayesian methods

Index Term-Uncontrolled  

Bayesian model averaging

Index Term-Uncontrolled  

Data borrowing

Index Term-Uncontrolled  

Multisource exchangeability models

Index Term-Uncontrolled  

Source-specific inference

Added Entry-Corporate Name  

University of Minnesota Biostatistics

Host Item Entry  

Dissertations Abstracts International. 85-02B.

Host Item Entry  

Dissertation Abstract International

Electronic Location and Access  

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Control Number  

joongbu:641625