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Statistical Methods for Longitudinal...

Manschot, Cole.

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Statistical Methods for Longitudinal Data with Biomedical Applications.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Statistical Methods for Longitudinal Data with Biomedical Applications./
Author:	Manschot, Cole.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2023,
Description:	168 p.
Notes:	Source: Dissertations Abstracts International, Volume: 84-12, Section: A.
Contained By:	Dissertations Abstracts International84-12A.
Subject:	Cancer. -
Online resource:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=30463935
ISBN:	9798379659509

Statistical Methods for Longitudinal Data with Biomedical Applications.
Manschot, Cole.

Statistical Methods for Longitudinal Data with Biomedical Applications. - Ann Arbor : ProQuest Dissertations & Theses, 2023 - 168 p.

Source: Dissertations Abstracts International, Volume: 84-12, Section: A.

Thesis (Ph.D.)--North Carolina State University, 2023.

This item must not be sold to any third party vendors.

Modern longitudinal data with biomedical applications has increasingly complex relationships between observations. Novel clinical trial designs focus on estimating the effect of an entire course of treatment, rather than a single time point, resulting in individuals sharing partial trajectories. Using the overlapping treatment sequences can yield more efficient estimates of the regime value but lacks a key methodology when these sequences are partially observed. Wearable device data are intensively measured and offer rich insights into personal health. The appeal of such objective measures of physical activity comes at an increasingly high computational burden. Ultimately, we aim to make efficient use of all available data. In this dissertation, we explore two application areas and develop implementable solutions for statistically efficient inference procedures. For sequential multiple assignment randomized trials, we propose an estimator for interim monitoring that leverages partially observed individuals to gain efficiency. We also propose a new approach to functional regression for intensively measured longitudinal outcomes that is computationally fast and statistically efficient. We then extend this approach to the case when some outcomes are missing by incorporating propensity weights in our estimation procedure.

ISBN: 9798379659509Subjects--Topical Terms:

634186
Cancer.

Statistical Methods for Longitudinal Data with Biomedical Applications.
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500 $a Source: Dissertations Abstracts International, Volume: 84-12, Section: A.
500 $a Advisor: Hector, Emily C.;Laber, Eric.
502 $a Thesis (Ph.D.)--North Carolina State University, 2023.
506 $a This item must not be sold to any third party vendors.
520 $a Modern longitudinal data with biomedical applications has increasingly complex relationships between observations. Novel clinical trial designs focus on estimating the effect of an entire course of treatment, rather than a single time point, resulting in individuals sharing partial trajectories. Using the overlapping treatment sequences can yield more efficient estimates of the regime value but lacks a key methodology when these sequences are partially observed. Wearable device data are intensively measured and offer rich insights into personal health. The appeal of such objective measures of physical activity comes at an increasingly high computational burden. Ultimately, we aim to make efficient use of all available data. In this dissertation, we explore two application areas and develop implementable solutions for statistically efficient inference procedures. For sequential multiple assignment randomized trials, we propose an estimator for interim monitoring that leverages partially observed individuals to gain efficiency. We also propose a new approach to functional regression for intensively measured longitudinal outcomes that is computationally fast and statistically efficient. We then extend this approach to the case when some outcomes are missing by incorporating propensity weights in our estimation procedure.
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650 4 $a Mean square errors. $3 3562318
650 4 $a Sample size. $3 3642155
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