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Statistical Methodologies for Trial ...

Ye, Fangshu.

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Statistical Methodologies for Trial Design With Network Meta-Analysis.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Statistical Methodologies for Trial Design With Network Meta-Analysis./
Author:	Ye, Fangshu.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2023,
Description:	95 p.
Notes:	Source: Dissertations Abstracts International, Volume: 85-03, Section: B.
Contained By:	Dissertations Abstracts International85-03B.
Subject:	Statistics. -
Online resource:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=30424551
ISBN:	9798380155182

Statistical Methodologies for Trial Design With Network Meta-Analysis.
Ye, Fangshu.

Statistical Methodologies for Trial Design With Network Meta-Analysis. - Ann Arbor : ProQuest Dissertations & Theses, 2023 - 95 p.

Source: Dissertations Abstracts International, Volume: 85-03, Section: B.

Thesis (Ph.D.)--Iowa State University, 2023.

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

Network meta-analysis (NMA) is a tool increasingly used in human and animal health to understand the comparative effect of interventions. One of the unique features of network meta-analysis is the ability to generate estimates of comparative efficacy when no direct clinical trial exists, which introduce the possibility to leverage evidence from the existing network of trial to inform the design of a new trial. Although there are methods proposed based on NMA to plan new trials, methodology for specific applications and the potential misuse of NMA in designing new trials are lacking. Our research focused on designing a new trial for two scenarios of comparison of interest: 1) two treatments in the existing network; 2) one treatment is in the existing network and the other one is not in the existing network. In Chapter 2, we propose an NMA-based method that can increase the power for a fixed total sample size or minimize the required total sample size to reach a pre-specified power when designing a new two-arm trial. In addition, we implement our proposed method into the R package OssaNMA and launch an R Shiny app for the convenience of the application. In Chapter 3, we investigate the potential inflation of type I error risk when a new trial is conducted only when, based on a p-value of the comparison in the existing network, a "promising" difference between two treatments is noticed. In Chapter 4, we consider how to design a new trial when the comparison of interest is between a treatment in the existing network and a treatment not in the existing network. All the methods are proposed from formula derivation and validated by simulation studies. Application of our work can provide insightful guidance for researchers to design a resource-saving new trial without misuse of NMA.

ISBN: 9798380155182Subjects--Topical Terms:

517247
Statistics.
Subjects--Index Terms:

Clinical trial design

Statistical Methodologies for Trial Design With Network Meta-Analysis.
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520 $a Network meta-analysis (NMA) is a tool increasingly used in human and animal health to understand the comparative effect of interventions. One of the unique features of network meta-analysis is the ability to generate estimates of comparative efficacy when no direct clinical trial exists, which introduce the possibility to leverage evidence from the existing network of trial to inform the design of a new trial. Although there are methods proposed based on NMA to plan new trials, methodology for specific applications and the potential misuse of NMA in designing new trials are lacking. Our research focused on designing a new trial for two scenarios of comparison of interest: 1) two treatments in the existing network; 2) one treatment is in the existing network and the other one is not in the existing network. In Chapter 2, we propose an NMA-based method that can increase the power for a fixed total sample size or minimize the required total sample size to reach a pre-specified power when designing a new two-arm trial. In addition, we implement our proposed method into the R package OssaNMA and launch an R Shiny app for the convenience of the application. In Chapter 3, we investigate the potential inflation of type I error risk when a new trial is conducted only when, based on a p-value of the comparison in the existing network, a "promising" difference between two treatments is noticed. In Chapter 4, we consider how to design a new trial when the comparison of interest is between a treatment in the existing network and a treatment not in the existing network. All the methods are proposed from formula derivation and validated by simulation studies. Application of our work can provide insightful guidance for researchers to design a resource-saving new trial without misuse of NMA.
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