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Advancing Statistical Methods for Ca...

Amoafo, Linda Appiagyei,

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Advancing Statistical Methods for Causal Inference and Environmental Exposure Assessment in Observational Studies /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Advancing Statistical Methods for Causal Inference and Environmental Exposure Assessment in Observational Studies // Linda Appiagyei Amoafo.
Author:	Amoafo, Linda Appiagyei,
Description:	1 electronic resource (107 pages)
Notes:	Source: Dissertations Abstracts International, Volume: 86-07, Section: B.
Contained By:	Dissertations Abstracts International86-07B.
Subject:	Biostatistics. -
Online resource:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=31482877
ISBN:	9798302160317

Advancing Statistical Methods for Causal Inference and Environmental Exposure Assessment in Observational Studies /
Amoafo, Linda Appiagyei,

Advancing Statistical Methods for Causal Inference and Environmental Exposure Assessment in Observational Studies /Linda Appiagyei Amoafo. - 1 electronic resource (107 pages)

Source: Dissertations Abstracts International, Volume: 86-07, Section: B.

This dissertation advances statistical methodologies for addressing critical issues in causal inference and environmental exposure assessment within observational studies, particularly in the context of public health. The first major contribution of this work is the development of a semiparametric sensitivity analysis approach aimed at addressing the impact of unmeasured confounding in observational studies with time-to-event outcomes. This novel approach, leveraging influence function-based techniques, offers a robust framework for enhancing the validity of causal inferences drawn from such studies.The second part of this dissertation ventures into the intersection of meteorological factors and suicide risk in Utah, broadening the understanding beyond traditional mental health paradigms. By employing a sophisticated statistical analysis that integrates suicide, air pollution, and meteorological data, this study reveals the complex and subtle effects of environmental variables on suicide. The findings not only highlight the significant association between meteorological conditions and suicide rates but also demonstrate the utility of statistical models in identifying critical environmental determinants of a preventable cause of death.The third segment explores the acute effects of mixed environmental exposures on health through the application of distributed lag models in a case-crossover design. This innovative approach allows for the estimation of immediate and delayed health impacts of various environmental exposures, addressing the complex temporal dynamics inherent in environmental epidemiology. The application of this method to the study of environmental mixtures and suicide mortality in Utah offers new insights into the role of environmental factors in public health, emphasizing the importance of considering mixed exposures in epidemiological research.Altogether, the research presented in this dissertation underscores the importance of advanced statistical methods in overcoming the limitations of observational studies, contributing to the development of more effective public health strategies and interventions.

English

ISBN: 9798302160317Subjects--Topical Terms:

1002712
Biostatistics.
Subjects--Index Terms:

Bayesian modeling

Advancing Statistical Methods for Causal Inference and Environmental Exposure Assessment in Observational Studies /
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245 1 0 $a Advancing Statistical Methods for Causal Inference and Environmental Exposure Assessment in Observational Studies / $c Linda Appiagyei Amoafo.
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500 $a Source: Dissertations Abstracts International, Volume: 86-07, Section: B.
500 $a Advisors: Zhang, Yue Committee members: Scharfstein, Daniel Oscar; Haaland, Benjamin; Bakian, Amanda Virginie; Bress, Adam.
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520 $a This dissertation advances statistical methodologies for addressing critical issues in causal inference and environmental exposure assessment within observational studies, particularly in the context of public health. The first major contribution of this work is the development of a semiparametric sensitivity analysis approach aimed at addressing the impact of unmeasured confounding in observational studies with time-to-event outcomes. This novel approach, leveraging influence function-based techniques, offers a robust framework for enhancing the validity of causal inferences drawn from such studies.The second part of this dissertation ventures into the intersection of meteorological factors and suicide risk in Utah, broadening the understanding beyond traditional mental health paradigms. By employing a sophisticated statistical analysis that integrates suicide, air pollution, and meteorological data, this study reveals the complex and subtle effects of environmental variables on suicide. The findings not only highlight the significant association between meteorological conditions and suicide rates but also demonstrate the utility of statistical models in identifying critical environmental determinants of a preventable cause of death.The third segment explores the acute effects of mixed environmental exposures on health through the application of distributed lag models in a case-crossover design. This innovative approach allows for the estimation of immediate and delayed health impacts of various environmental exposures, addressing the complex temporal dynamics inherent in environmental epidemiology. The application of this method to the study of environmental mixtures and suicide mortality in Utah offers new insights into the role of environmental factors in public health, emphasizing the importance of considering mixed exposures in epidemiological research.Altogether, the research presented in this dissertation underscores the importance of advanced statistical methods in overcoming the limitations of observational studies, contributing to the development of more effective public health strategies and interventions.
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650 4 $a Statistics. $3 517247
650 4 $a Environmental health. $3 543032
650 4 $a Epidemiology. $3 568544
653 $a Bayesian modeling
653 $a Causal inferences
653 $a Confounding
653 $a Distributed lag models
653 $a Prostate cancer
690 $a 0308
690 $a 0463
690 $a 0766
690 $a 0470
710 2 $a The University of Utah. $b Population Health Sciences. $e degree granting institution. $3 3827173
720 1 $a Zhang, Yue $e degree supervisor.
773 0 $t Dissertations Abstracts International $g 86-07B.
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791 $a Ph.D.
792 $a 2024
856 4 0 $u https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=31482877