國立高雄大學圖資館 |

Innovative Statistical Methods for Improving Clinical Trial Design and Personalized Medicine /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Innovative Statistical Methods for Improving Clinical Trial Design and Personalized Medicine /Peng Yang.
作者:	Yang, Peng,
面頁冊數:	1 electronic resource (223 pages)
附註:	Source: Dissertations Abstracts International, Volume: 87-02, Section: B.
附註:	Advisors: Yuan, Ying; Li, Ziyi; Kimmel, Marek Committee members: Li, Meng; Yao, Vicky.
Contained By:	Dissertations Abstracts International87-02B.
標題:	Statistics.
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=32260667
ISBN:	9798290947044

Innovative Statistical Methods for Improving Clinical Trial Design and Personalized Medicine /
Yang, Peng,

Innovative Statistical Methods for Improving Clinical Trial Design and Personalized Medicine /Peng Yang. - 1 electronic resource (223 pages)

Source: Dissertations Abstracts International, Volume: 87-02, Section: B.

Clinical trials are pivotal for advancing medical treatments, yet they are often burdened by high costs, lengthy timelines, and substantial failure rates, particularly in oncology. These challenges underscore the need for innovative approaches to improve trial efficiency and facilitate personalized treatment strategies. This thesis introduces novel statistical methods and computational tools designed to address these issues by optimizing clinical trial design and enhancing personalized medicine.Chapter 1 presents a multiple-dose randomized clinical trial design aimed at identifying the optimal dose that maximizes the benefit-risk tradeoff. We generalize the standard definitions of type I error and power to accommodate the unique aspects of dose optimization and derive a decision rule, along with an algorithm, to determine the optimal sample size. This design, referred to as MERIT (Multiple-dosE RandomIzed Trial design for dose optimization based on toxicity and efficacy), incorporates Bayesian interim rules to allow for early trial termination due to toxicity or futility. Simulation studies show that MERIT offers favorable operating characteristics, with a sample size of 20 to 40 per dosage arm generally providing reasonable power and type I errors to ensure patient safety and benefit. To support implementation, we provide the MERIT design software, available at www.trialdesign.org.In Chapter 2, we introduce the self-adapting mixture (SAM) prior, a novel method for dynamically incorporating historical data into ongoing trials. SAM priors are data-driven and self-adjusting, adaptively weighing the informative or non-informative prior component based on evidence of prior-data conflict. This results in dynamic information borrowing, with SAM priors demonstrating desirable properties in both finite and large samples and achieving consistency in information borrowing. Additionally, SAM priors are computationally efficient, data-driven, and calibration-free, minimizing the risk of data dredging. Numerical studies show that SAM priors outperform existing methods in addressing prior-data conflicts effectively. We have developed an R package, "SAMprior", and a web application, both freely available on CRAN.Chapter 3 introduces a novel Bayesian adaptive design for avatar-driven cancer clinical trials, which uses mouse or laboratory animal grafts to create personalized tumor models to guide patient treatment. This design adaptively selects the optimal treatment for each patient by jointly modeling avatar tumor growth and patient clinical outcomes, including toxicity and progression-free survival, to capture the interaction between avatars and their source patients. A latent-class model is used to account for the possibility that avatars may only be effective surrogates for treatment effects in a subset of patients.In Chapter 4, we explore intratumor heterogeneity (ITH) in cancer, a key factor in treatment response, using multi-region gene expression data. We propose ICeITH, a Bayesian hierarchical model that incorporates cell type profiles as prior knowledge to decompose mixed bulk data while accounting for within-subject correlations among tumor samples. ICeITH quantifies ITH by assessing the variability in targeted cellular compositions. We also demonstrate ICeITH's ability to stratify patients based on their ITH scores and link these estimates to survival outcomes in two non-small cell lung cancer datasets.

English

ISBN: 9798290947044Subjects--Topical Terms:

182057
Statistics.
Subjects--Index Terms:

Bayesian statistics

Innovative Statistical Methods for Improving Clinical Trial Design and Personalized Medicine /
LDR:05167nmm a22004933i 4500 001 693773
005 20260511063023.5
006 m o d
007 cr|nu||||||||
008 260819s2025 miu||||||m |||||||eng d
020 $a 9798290947044
035 $a (MiAaPQD)AAI32260667
035 $a (MiAaPQD)0187rice5658Yang
035 $a AAI32260667
040 $a MiAaPQD $b eng $c MiAaPQD $e rda
100 1 $a Yang, Peng, $e author. $3 1010445
245 1 0 $a Innovative Statistical Methods for Improving Clinical Trial Design and Personalized Medicine / $c Peng Yang.
264 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2025
300 $a 1 electronic resource (223 pages)
336 $a text $b txt $2 rdacontent
337 $a computer $b c $2 rdamedia
338 $a online resource $b cr $2 rdacarrier
500 $a Source: Dissertations Abstracts International, Volume: 87-02, Section: B.
500 $a Advisors: Yuan, Ying; Li, Ziyi; Kimmel, Marek Committee members: Li, Meng; Yao, Vicky.
502 $b Ph.D. $c Rice University $d 2025.
520 $a Clinical trials are pivotal for advancing medical treatments, yet they are often burdened by high costs, lengthy timelines, and substantial failure rates, particularly in oncology. These challenges underscore the need for innovative approaches to improve trial efficiency and facilitate personalized treatment strategies. This thesis introduces novel statistical methods and computational tools designed to address these issues by optimizing clinical trial design and enhancing personalized medicine.Chapter 1 presents a multiple-dose randomized clinical trial design aimed at identifying the optimal dose that maximizes the benefit-risk tradeoff. We generalize the standard definitions of type I error and power to accommodate the unique aspects of dose optimization and derive a decision rule, along with an algorithm, to determine the optimal sample size. This design, referred to as MERIT (Multiple-dosE RandomIzed Trial design for dose optimization based on toxicity and efficacy), incorporates Bayesian interim rules to allow for early trial termination due to toxicity or futility. Simulation studies show that MERIT offers favorable operating characteristics, with a sample size of 20 to 40 per dosage arm generally providing reasonable power and type I errors to ensure patient safety and benefit. To support implementation, we provide the MERIT design software, available at www.trialdesign.org.In Chapter 2, we introduce the self-adapting mixture (SAM) prior, a novel method for dynamically incorporating historical data into ongoing trials. SAM priors are data-driven and self-adjusting, adaptively weighing the informative or non-informative prior component based on evidence of prior-data conflict. This results in dynamic information borrowing, with SAM priors demonstrating desirable properties in both finite and large samples and achieving consistency in information borrowing. Additionally, SAM priors are computationally efficient, data-driven, and calibration-free, minimizing the risk of data dredging. Numerical studies show that SAM priors outperform existing methods in addressing prior-data conflicts effectively. We have developed an R package, "SAMprior", and a web application, both freely available on CRAN.Chapter 3 introduces a novel Bayesian adaptive design for avatar-driven cancer clinical trials, which uses mouse or laboratory animal grafts to create personalized tumor models to guide patient treatment. This design adaptively selects the optimal treatment for each patient by jointly modeling avatar tumor growth and patient clinical outcomes, including toxicity and progression-free survival, to capture the interaction between avatars and their source patients. A latent-class model is used to account for the possibility that avatars may only be effective surrogates for treatment effects in a subset of patients.In Chapter 4, we explore intratumor heterogeneity (ITH) in cancer, a key factor in treatment response, using multi-region gene expression data. We propose ICeITH, a Bayesian hierarchical model that incorporates cell type profiles as prior knowledge to decompose mixed bulk data while accounting for within-subject correlations among tumor samples. ICeITH quantifies ITH by assessing the variability in targeted cellular compositions. We also demonstrate ICeITH's ability to stratify patients based on their ITH scores and link these estimates to survival outcomes in two non-small cell lung cancer datasets.
546 $a English
590 $a School code: 0187
650 4 $a Statistics. $3 182057
650 4 $a Biostatistics. $3 339693
650 4 $a Clinical psychology. $3 328102
653 $a Bayesian statistics
653 $a Bayesian adaptive design
653 $a Clinical trial design
653 $a Dose optimization
653 $a Information borrowing
653 $a Personalized medicine
653 $a Statistical learning
690 $a 0463
690 $a 0308
690 $a 0622
710 2 $a Rice University. $b Statistics. $e degree granting institution. $3 990181
720 1 $a Yuan, Ying $e degree supervisor.
720 1 $a Li, Ziyi $e degree supervisor.
720 1 $a Kimmel, Marek $e degree supervisor.
773 0 $t Dissertations Abstracts International $g 87-02B.
790 $a 0187
791 $a Ph.D.
792 $a 2025
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=32260667