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Generating Reliable and Responsive Observational Evidence : = Reducing Pre-Analysis Bias.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Generating Reliable and Responsive Observational Evidence :/
其他題名:	Reducing Pre-Analysis Bias.
作者:	Ostropolets, Anna.
面頁冊數:	1 online resource (481 pages)
附註:	Source: Dissertations Abstracts International, Volume: 84-07, Section: B.
Contained By:	Dissertations Abstracts International84-07B.
標題:	Bioinformatics. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=30244081click for full text (PQDT)
ISBN:	9798368423593

Generating Reliable and Responsive Observational Evidence : = Reducing Pre-Analysis Bias.
Ostropolets, Anna.

Generating Reliable and Responsive Observational Evidence :Reducing Pre-Analysis Bias. - 1 online resource (481 pages)

Source: Dissertations Abstracts International, Volume: 84-07, Section: B.

Thesis (Ph.D.)--Columbia University, 2023.

Includes bibliographical references

A growing body of evidence generated from observational data has demonstrated the potential to influence decision-making and improve patient outcomes. For observational evidence to be actionable, however, it must be generated reliably and in a timely manner. Large distributed observational data networks enable research on diverse patient populations at scale and develop new sound methods to improve reproducibility and robustness of real-world evidence. Nevertheless, the problems of generalizability, portability and scalability persist and compound. As analytical methods only partially address bias, reliable observational research (especially in networks) must address the bias at the design stage (i.e., pre-analysis bias) including the strategies for identifying patients of interest and defining comparators.This thesis synthesizes and enumerates a set of challenges to addressing pre-analysis bias in observational studies and presents mixed-methods approaches and informatics solutions for overcoming a number of those obstacles. We develop frameworks, methods and tools for scalable and reliable phenotyping including data source granularity estimation, comprehensive concept set selection, index date specification, and structured data-based patient review for phenotype evaluation. We cover the research on potential bias in the unexposed comparator definition including systematic background rates estimation and interpretation, and definition and evaluation of the unexposed comparator.We propose that the use of standardized approaches and methods as described in this thesis not only improves reliability but also increases responsiveness of observational evidence. To test this hypothesis, we designed and piloted a Data Consult Service - a service that generates new on-demand evidence at the bedside. We demonstrate that it is feasible to generate reliable evidence to address clinicians' information needs in a robust and timely fashion and provide our analysis of the current limitations and future steps needed to scale such a service.

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

Mode of access: World Wide Web

ISBN: 9798368423593Subjects--Topical Terms:

553671
Bioinformatics.
Subjects--Index Terms:

Pre-analysis biasIndex Terms--Genre/Form:

542853
Electronic books.

Generating Reliable and Responsive Observational Evidence : = Reducing Pre-Analysis Bias.
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