東華大學圖書館 |

語系: 繁體中文

說明(常見問題)

回圖書館首頁

手機版館藏查詢

登入

回首頁

切換: 標籤 | MARC模式 | ISBD

Bayesian Methods for Decision Making...

Hua, William.

FindBook

Google Book

Amazon

博客來

Bayesian Methods for Decision Making in Biomedical Applications.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Bayesian Methods for Decision Making in Biomedical Applications./
作者:	Hua, William.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2020,
面頁冊數:	162 p.
附註:	Source: Dissertations Abstracts International, Volume: 82-10, Section: B.
Contained By:	Dissertations Abstracts International82-10B.
標題:	Statistics. -
電子資源:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28419957
ISBN:	9798582557586

Bayesian Methods for Decision Making in Biomedical Applications.
Hua, William.

Bayesian Methods for Decision Making in Biomedical Applications. - Ann Arbor : ProQuest Dissertations & Theses, 2020 - 162 p.

Source: Dissertations Abstracts International, Volume: 82-10, Section: B.

Thesis (Ph.D.)--The Johns Hopkins University, 2020.

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

This dissertation focuses on developing Bayesian survival analysis methodology for optimizing decision making and treatment methods in a variety of biomedical applications. First, we develop a flexible Bayesian nonparametric regression model based on a dependent Dirichlet process and Gaussian process, DDP-GP, for optimizing precision dosing of intravenous busulfan in allogenic stem cell transplantation. Our analyses of a dataset of 151 patients identified optimal therapeutic dosage intervals that maximizes patient survival outcomes and varies substantively with age and complete remission status. Extensive simulations to evaluate the DDP-GP model in similar settings showed that its performance compares favorably to alternative methods. We provide an R package, DDPGPSurv, that implements the DDP-GP model for a wide range of survival regression analyses.The second main contribution of this dissertation is the development of personalized dynamic treatment regimes (DTR) in continuous time. Traditional statistical methods for DTRs usually focus on estimating the optimal treatment or dosage at each given medical intervention, but overlook the important question of "when this intervention should happen." We fill this gap by building a generative model for a sequence of medical interventions with a marked temporal point process (MTPP) and embedding this into a Bayesian joint framework where the other components model longitudinal medical measurements and time-to-event data. Moreover, we propose a policy gradient method to learn the personalized optimal clinical decision that maximizes patient survival by interacting the MTPP with the model on clinical observations while accounting for uncertainties in clinical observations. A signature application is to schedule follow-up visitations and assign a dosage at each visitation for patients after kidney transplantation. We demonstrate that the personalized decisions made by our method are interpretable and help improve patient survival, and provide an R package, doct, that broadly implements our framework.Lastly, we introduce a Bayesian semiparametric model for learning biomarker trajectories and change points in Alzheimer's disease (AD). Through simulation and real data studies, we show that our model is able to reliably detect a pre-diagnosis longitudinal change point, evaluate the probability of AD progression, and account for heterogeneity by clustering subjects using longitudinal and diagnosis time data.

ISBN: 9798582557586Subjects--Topical Terms:

517247
Statistics.
Subjects--Index Terms:

Bayesian statistics

Bayesian Methods for Decision Making in Biomedical Applications.
LDR:03601nmm a2200349 4500 001 2279303
005 20210730131635.5
008 220723s2020 ||||||||||||||||| ||eng d
020 $a 9798582557586
035 $a (MiAaPQ)AAI28419957
035 $a (MiAaPQ)0098vireo6030Hua
035 $a AAI28419957
040 $a MiAaPQ $c MiAaPQ
100 1 $a Hua, William. $3 3557746
245 1 0 $a Bayesian Methods for Decision Making in Biomedical Applications.
260 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2020
300 $a 162 p.
500 $a Source: Dissertations Abstracts International, Volume: 82-10, Section: B.
500 $a Advisor: Athreya, Avanti.
502 $a Thesis (Ph.D.)--The Johns Hopkins University, 2020.
506 $a This item must not be sold to any third party vendors.
520 $a This dissertation focuses on developing Bayesian survival analysis methodology for optimizing decision making and treatment methods in a variety of biomedical applications. First, we develop a flexible Bayesian nonparametric regression model based on a dependent Dirichlet process and Gaussian process, DDP-GP, for optimizing precision dosing of intravenous busulfan in allogenic stem cell transplantation. Our analyses of a dataset of 151 patients identified optimal therapeutic dosage intervals that maximizes patient survival outcomes and varies substantively with age and complete remission status. Extensive simulations to evaluate the DDP-GP model in similar settings showed that its performance compares favorably to alternative methods. We provide an R package, DDPGPSurv, that implements the DDP-GP model for a wide range of survival regression analyses.The second main contribution of this dissertation is the development of personalized dynamic treatment regimes (DTR) in continuous time. Traditional statistical methods for DTRs usually focus on estimating the optimal treatment or dosage at each given medical intervention, but overlook the important question of "when this intervention should happen." We fill this gap by building a generative model for a sequence of medical interventions with a marked temporal point process (MTPP) and embedding this into a Bayesian joint framework where the other components model longitudinal medical measurements and time-to-event data. Moreover, we propose a policy gradient method to learn the personalized optimal clinical decision that maximizes patient survival by interacting the MTPP with the model on clinical observations while accounting for uncertainties in clinical observations. A signature application is to schedule follow-up visitations and assign a dosage at each visitation for patients after kidney transplantation. We demonstrate that the personalized decisions made by our method are interpretable and help improve patient survival, and provide an R package, doct, that broadly implements our framework.Lastly, we introduce a Bayesian semiparametric model for learning biomarker trajectories and change points in Alzheimer's disease (AD). Through simulation and real data studies, we show that our model is able to reliably detect a pre-diagnosis longitudinal change point, evaluate the probability of AD progression, and account for heterogeneity by clustering subjects using longitudinal and diagnosis time data.
590 $a School code: 0098.
650 4 $a Statistics. $3 517247
650 4 $a Biostatistics. $3 1002712
653 $a Bayesian statistics
653 $a Biostatistics
653 $a Survival analysis
690 $a 0463
690 $a 0308
710 2 $a The Johns Hopkins University. $b Applied Mathematics and Statistics. $3 3550441
773 0 $t Dissertations Abstracts International $g 82-10B.
790 $a 0098
791 $a Ph.D.
792 $a 2020
793 $a English
856 4 0 $u https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28419957