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Advances in Data-Driven Research Met...

Lawson, Michael T.

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Advances in Data-Driven Research Methodology for Precision Public Health.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Advances in Data-Driven Research Methodology for Precision Public Health./
Author:	Lawson, Michael T.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2019,
Description:	126 p.
Notes:	Source: Dissertations Abstracts International, Volume: 80-12, Section: B.
Contained By:	Dissertations Abstracts International80-12B.
Subject:	Biostatistics. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=13857154
ISBN:	9781392202180

Advances in Data-Driven Research Methodology for Precision Public Health.
Lawson, Michael T.

Advances in Data-Driven Research Methodology for Precision Public Health. - Ann Arbor : ProQuest Dissertations & Theses, 2019 - 126 p.

Source: Dissertations Abstracts International, Volume: 80-12, Section: B.

Thesis (Ph.D.)--The University of North Carolina at Chapel Hill, 2019.

This item must not be added to any third party search indexes.

The rise of precision medicine has ushered in manifold opportunities and challenges, many of them linked. For instance: precision medicine offers an avenue to revisit assumption-rich, knowledge-driven research practices, but requires careful and creative thinking to replace them. In this manuscript, we turn our attention to three such areas of interest: subgroup determination, modeling of dynamical systems, and accounting for measurement error. In each case, we construct a statistical and machine learning framework for the problem at hand, develop methodology to address it, and present theoretical and numerical justifications for the methodology.In the first chapter, we develop a data-driven method for subgroup determination in a clinical trial of treatment or intervention, where subgroups are based on predicted efficacy of treatment and not based on a limited number of a priori-specified markers. The proposed subgroup determination method is illustrated in a trial of a lifestyle intervention in type 1 diabetes, where we use it to determine subgroups who are expected to benefit from intervention and from control conditions. In the second chapter, we formulate a fully nonparametric stochastic differential equation model that performs model selection for factors affecting both the mean and variability of a dynamic process. The model is applied to data arising from a type 1 diabetes trial. In the third chapter, we turn our attention to developing model-agnostic influence statistics to assess the impact of observations' mismeasurement on analysis results. This method is illustrated in detail in three different settings, one of which is a study of water quality with a complex mechanism of measurement error.

ISBN: 9781392202180Subjects--Topical Terms:

1002712
Biostatistics.

Advances in Data-Driven Research Methodology for Precision Public Health.
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500 $a Source: Dissertations Abstracts International, Volume: 80-12, Section: B.
500 $a Publisher info.: Dissertation/Thesis.
500 $a Advisor: Kosorok, Michael R.
502 $a Thesis (Ph.D.)--The University of North Carolina at Chapel Hill, 2019.
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520 $a The rise of precision medicine has ushered in manifold opportunities and challenges, many of them linked. For instance: precision medicine offers an avenue to revisit assumption-rich, knowledge-driven research practices, but requires careful and creative thinking to replace them. In this manuscript, we turn our attention to three such areas of interest: subgroup determination, modeling of dynamical systems, and accounting for measurement error. In each case, we construct a statistical and machine learning framework for the problem at hand, develop methodology to address it, and present theoretical and numerical justifications for the methodology.In the first chapter, we develop a data-driven method for subgroup determination in a clinical trial of treatment or intervention, where subgroups are based on predicted efficacy of treatment and not based on a limited number of a priori-specified markers. The proposed subgroup determination method is illustrated in a trial of a lifestyle intervention in type 1 diabetes, where we use it to determine subgroups who are expected to benefit from intervention and from control conditions. In the second chapter, we formulate a fully nonparametric stochastic differential equation model that performs model selection for factors affecting both the mean and variability of a dynamic process. The model is applied to data arising from a type 1 diabetes trial. In the third chapter, we turn our attention to developing model-agnostic influence statistics to assess the impact of observations' mismeasurement on analysis results. This method is illustrated in detail in three different settings, one of which is a study of water quality with a complex mechanism of measurement error.
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