Literature DB >> 21905067

Bayesian adaptive clinical trials: a dream for statisticians only?

Sylvie Chevret1.   

Abstract

Adaptive or 'flexible' designs have emerged, mostly within frequentist frameworks, as an effective way to speed up the therapeutic evaluation process. Because of their flexibility, Bayesian methods have also been proposed for Phase I through Phase III adaptive trials; however, it has been reported that they are poorly used in practice. We aim to describe the international scientific production of Bayesian clinical trials by investigating the actual development and use of Bayesian 'adaptive' methods in the setting of clinical trials. A bibliometric study was conducted using the PubMed and Science Citation Index-Expanded databases. Most of the references found were biostatistical papers from various teams around the world. Most of the authors were from the US, and a large proportion was from the MD Anderson Cancer Center (University of Texas, Houston, TX). The spread and use of these articles depended heavily on their topic, with 3.1% of the biostatistical articles accumulating at least 25 citations within 5 years of their publication compared with 15% of the reviews and 32% of the clinical articles. We also examined the reasons for the limited use of Bayesian adaptive design methods in clinical trials and the areas of current and future research to address these challenges. Efforts to promote Bayesian approaches among statisticians and clinicians appear necessary.
Copyright © 2011 John Wiley & Sons, Ltd.

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Year:  2011        PMID: 21905067     DOI: 10.1002/sim.4363

Source DB:  PubMed          Journal:  Stat Med        ISSN: 0277-6715            Impact factor:   2.373


  24 in total

1.  Building efficient comparative effectiveness trials through adaptive designs, utility functions, and accrual rate optimization: finding the sweet spot.

Authors:  Byron J Gajewski; Scott M Berry; Melanie Quintana; Mamatha Pasnoor; Mazen Dimachkie; Laura Herbelin; Richard Barohn
Journal:  Stat Med       Date:  2015-01-07       Impact factor: 2.373

2.  An overview of the adaptive designs accelerating promising trials into treatments (ADAPT-IT) project.

Authors:  William J Meurer; Roger J Lewis; Danilo Tagle; Michael D Fetters; Laurie Legocki; Scott Berry; Jason Connor; Valerie Durkalski; Jordan Elm; Wenle Zhao; Shirley Frederiksen; Robert Silbergleit; Yuko Palesch; Donald A Berry; William G Barsan
Journal:  Ann Emerg Med       Date:  2012-03-15       Impact factor: 5.721

Review 3.  Cardiovascular drug discovery: a perspective from a research-based pharmaceutical company.

Authors:  G Gromo; J Mann; J D Fitzgerald
Journal:  Cold Spring Harb Perspect Med       Date:  2014-06-02       Impact factor: 6.915

Review 4.  Model-Assisted Designs for Early-Phase Clinical Trials: Simplicity Meets Superiority.

Authors:  Ying Yuan; J Jack Lee; Susan G Hilsenbeck
Journal:  JCO Precis Oncol       Date:  2019-10-24

5.  Bayesian clinical trials at The University of Texas MD Anderson Cancer Center: An update.

Authors:  Rebecca S Slack Tidwell; S Andrew Peng; Minxing Chen; Diane D Liu; Ying Yuan; J Jack Lee
Journal:  Clin Trials       Date:  2019-08-26       Impact factor: 2.486

Review 6.  Bayesian clinical trials in action.

Authors:  J Jack Lee; Caleb T Chu
Journal:  Stat Med       Date:  2012-06-18       Impact factor: 2.373

7.  Commentary on Hey and Kimmelman.

Authors:  J Jack Lee
Journal:  Clin Trials       Date:  2015-02-03       Impact factor: 2.486

8.  Adaptive Clinical Trials: Overview of Early-Phase Designs and Challenges.

Authors:  Olga Marchenko; Valerii Fedorov; J Jack Lee; Christy Nolan; José Pinheiro
Journal:  Ther Innov Regul Sci       Date:  2013-11-26       Impact factor: 1.778

Review 9.  Adaptive designs for dual-agent phase I dose-escalation studies.

Authors:  Jennifer A Harrington; Graham M Wheeler; Michael J Sweeting; Adrian P Mander; Duncan I Jodrell
Journal:  Nat Rev Clin Oncol       Date:  2013-03-19       Impact factor: 66.675

10.  A comparative study of Bayesian optimal interval (BOIN) design with interval 3+3 (i3+3) design for phase I oncology dose-finding trials.

Authors:  Yanhong Zhou; Ruobing Li; Fangrong Yan; J Jack Lee; Ying Yuan
Journal:  Stat Biopharm Res       Date:  2020-09-14       Impact factor: 1.452

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