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Large Scale Observational Analytics for Clinical Evidence Generation.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Large Scale Observational Analytics for Clinical Evidence Generation./
作者:	Tian, Yuxi.
面頁冊數:	1 online resource (248 pages)
附註:	Source: Dissertations Abstracts International, Volume: 85-01, Section: B.
Contained By:	Dissertations Abstracts International85-01B.
標題:	Bioinformatics. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=27996969click for full text (PQDT)
ISBN:	9798379916541

Large Scale Observational Analytics for Clinical Evidence Generation.
Tian, Yuxi.

Large Scale Observational Analytics for Clinical Evidence Generation. - 1 online resource (248 pages)

Source: Dissertations Abstracts International, Volume: 85-01, Section: B.

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

Includes bibliographical references

Longitudinal observational health data are rapidly becoming standardized and consolidated at massive scale. However, the large size and observational nature of this data create infrastructural, statistical, and computational challenges to their utilization for generating reliable clinical evidence. I first review principles of observational research and various methodological and statistical tools used to conduct modern observational studies. I then discuss methodological advancements and their clinical implementations in large scale observational health analytics. I introduce a framework for evaluating propensity score methods that are a central tool in addressing confounding in non-randomized studies. This framework incorporates simulations that model real-world survival data and negative control experiments. I adapt my evaluation framework to probe the real-world prevalence and consequences of "instrumental variables" that unduly dominate propensity score models and bias clinical effect size estimates. I then compare propensity score adjustment methods in research evaluating spline functions for multiple treatment settings. Next, I turn to statistical computing challenges that hinder the application of high-quality methods in large data. I utilize graphics processing unit (GPU) programming to accelerate logistic regression, a staple statistical regression used for propensity score estimation, in the high-dimensional regimes necessitated by the largest health databases. Finally, I conduct clinical studies using tools developed through the Observational Health Data Sciences and Informatics (OHDSI) community that allow large-scale and high-quality observational studies to be conducted with previously unattainable efficiency. In one study, I analyze the comparative effectiveness of two popular osteoporosis drugs in preventing fractures and in regards to concerning drug-related adverse events. In a second study, I address the highly controversial use of recombinant human bone morphogenetic protein 2 in spinal fusion surgeries. In a third study, I report on the comparative effectiveness of antidepressant treatments in preventing suicide and suicidal ideation within a novel all-by-all paradigm of conducting many hypothesis simultaneously within a medical domain. In a final study, I evaluate the effectiveness of generic vs branded medications of many drugs across three medical domains with regards to death and major cardiovascular events. I conclude with thoughts about future research and progress in observational medical science.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2023

Mode of access: World Wide Web

ISBN: 9798379916541Subjects--Topical Terms:

553671
Bioinformatics.
Subjects--Index Terms:

BiomathematicsIndex Terms--Genre/Form:

542853
Electronic books.

Large Scale Observational Analytics for Clinical Evidence Generation.
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