Developing statistical methodologies to support embedding clinical trials within learning health systems
department or school: Medicine, Faculty of
department or school: Population and Public Health (SPPH), School of
data provider: DSpace
degree campus: UBCV
creator: Zhan, Denghuang
issued: 2025-04-23T14:55:26Z
issued: 2025
related degree: Doctor of Philosophy - PhD
degree grantor: University of British Columbia
description: A learning health system (LHS) is a framework designed to generate and apply evidence for improving healthcare decisions and outcomes. In practice, bridging the gap from data generation to evidence is challenging, often constrained by the lack of statistical methodologies to utilize available LHS data optimally. This dissertation contributes to methodologies for embedding clinical trials into learning health systems, addressing challenges in adaptive trial designs, real-world data integration, and personalized medicine. First, we developed an R package, BayesAET, to evaluate Bayesian adaptive enrichment designs via simulation, allowing for early termination, flexible randomization, and incorporation of external information. This tool supports efficient trial designs tailored to subpopulations, facilitating more personalized treatment recommendations. Second, we investigated the properties of Bayesian group-sequential noninferiority trials, highlighting trade-offs in power, sample size, and interoperability compared to Frequentist designs. Using plasmode simulations based on a real-world dataset, the study demonstrated Bayesian groupsequential designs reduce expected sample sizes significantly but careful consideration is required to plan the number of interim analyses to optimize the specific design. We found that in our test scenarios, response adaptive randomization (RAR) resulted in more participants being randomized to the inferior treatment in group-sequential non-inferiority trials, emphasizing the need for careful evaluation of RAR’s appropriateness in such settings. Third, we introduced a novel method for efficiently constructing a prior distribution for a treatment effect given two datasets in which confounding is present in one dataset due to missing covariates. This method demonstrates comparable performance to Bayesian MCMC approaches while significantly improving computational efficiency, making it practical for evolving LHS environments. Lastly, we con…
aggregated CHO: https://circle.library.ubc.ca/rest/handle/2429/90720
note: DEVELOPING STATISTICALMETHODOLOGIES TO SUPPORTEMBEDDING CLINICAL TRIALSWITHIN LEARNING HEALTHSYSTEMSbyDenghuang ZhanB.Eng., Huazhong University of Science and Technology, 2014M.E.Sc., Western University, 2017A THESIS SUBMITTED IN PARTIAL FULFILLMENT OFTHE REQUIREMENTS FOR THE DEGREE OFDOCTOR OF PHILOSOPHYinThe Faculty of Graduate and Postdoctoral Studies(Population and Public Health)THE UNIVERSITY OF BRITISH COLUMBIA(Vancouver)April 2025© Denghuang Zhan 2025The following individuals certify that they have read, and recommend tothe Faculty of Graduate and Postdoctoral Studies for acceptance, the thesisentitled:Developing statistical methodologies to support embeddingclinical trials within learning health systemssubmitted by Denghuang Zhan in partial fulfillment of the requirementsfor the degree of Doctor of Philosophyin Population and Public HealthExamining Committee:Hubert Wong, Associate Professor, School of Population and Public Health,UBCSupervisorFidel Vila-Rodriguez, Associate Professor, Department of Psychiatry, UBCCo-supervisorMohammad Ehsanul (Ehsan) Karim, Assistant Professor, School of Popu-lation and Public Health, UBCSupervisory Committee MemberJoel Singer, Professor, School of Population and Public Health, UBCUniversity ExaminerRollin Brant, Professor, Department of Statistics, UBCUniversity ExaminerGeorge Tomlinson, Senior Scientist, Toronto General Hospital Research In-stitute, University Health NetworkExternal ExamineriiAbstractA learning health system (LHS) is a framework designed to generate and ap-ply evidence for improving healthcare decisions and outcomes. In practice,bridging the gap from data generation to evidence is challenging, often con-strained by the lack of statistical methodologies to utilize available LHS dataoptimally. This dissertation contributes to methodologies for embeddingclinical trials into learning health systems, addressing challenges in adaptivetrial designs, real-world data integration, and personalized medicine.First, we…
has type: Thesis/Dissertation
date issued: 2025-05
is shown at: 10.14288/1.0448505
language: eng
degree discipline: Population and Public Health
provider: Vancouver : University of British Columbia Library
publisher: University of British Columbia
rights: Attribution-NonCommercial-NoDerivatives 4.0 International
rights URI: http://creativecommons.org/licenses/by-nc-nd/4.0/
scholar level: Graduate
contributor: Wong, Hubert
contributor: Vila-Rodriguez, Fidel
type: Text
identifier URI: http://hdl.handle.net/2429/90720