자료유형  

 학위논문

Control Number  

0016934021

International Standard Book Number  

9798380136570

Dewey Decimal Classification Number  

574

Main Entry-Personal Name  

Rosin, Samuel P.

Publication, Distribution, etc. (Imprint  

[S.l.] : The University of North Carolina at Chapel Hill., 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: Hudgens, Michael G.

Dissertation Note  

Thesis (Ph.D.)--The University of North Carolina at Chapel Hill, 2023.

Restrictions on Access Note  

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

Summary, Etc.  

요약In the COVID-19 pandemic, there was great interest in population seroprevalence estimation of individuals with antibodies against SARS-CoV-2 and in evaluation of antibodies as surrogate markers for vaccine efficacy. In the first paper, methods for estimation of seroprevalence from surveys which can have selection bias and serologic tests which can have measurement error are presented. These challenges are addressed with leveraging of auxiliary data, e.g., population census data, and of laboratory studies of false positive and false negative rates. Direct standardization is used for development of nonparametric and parametric seroprevalence estimators. The estimators are proven consistent and asymptotically normal. Simulation studies demonstrate performance across a variety of selection and misclassification biases scenarios. The proposed methods are applied to SARS-CoV-2 seroprevalence studies in New York City, Belgium, and North Carolina.Drawing simple comparisons of COVID-19 vaccine trial efficacy estimates is problematic without considering factors affecting trial context and design, including characteristics of a study's population (Rapaka et al., 2022). A meta-analytic paradigm for surrogate endpoint evaluation entails estimating an association between the treatment effects on the surrogate and clinical endpoints, respectively, using data from multiple clinical trials. This approach can estimate the association between vaccine induced anti-SARS-CoV-2 antibodies and vaccine efficacy against symptomatic COVID-19 illness. In the second paper, multiple vaccine trials are standardized to a common target population. Meta-analytic causal association parameters, estimators, and the asymptotic distributions of the estimators are considered. A hypothesis test of an implication of a conditional exchangeability assumption is proposed. Simulation studies demonstrate the methods in scenarios motivated by data from several U.S. government Phase 3 SARS-CoV-2 vaccine trials. 

Subject Added Entry-Topical Term  

Biostatistics.

Subject Added Entry-Topical Term  

Public health.

Subject Added Entry-Topical Term  

Epidemiology.

Index Term-Uncontrolled  

Causal inference

Index Term-Uncontrolled  

COVID-19

Index Term-Uncontrolled  

Diagnostic statistics

Index Term-Uncontrolled  

Estimating equations

Index Term-Uncontrolled  

Infectious disease

Added Entry-Corporate Name  

The University of North Carolina at Chapel Hill 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:641078