Literature DB >> 32923856

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

Ying Yuan1, J Jack Lee1, Susan G Hilsenbeck2.   

Abstract

Drug development enterprise is struggling because of prohibitively high costs and slow progress. There is urgent need for adoption of novel adaptive designs to improve the efficiency and success of clinical trials. A major barrier is that many conventional designs are inadequate for modern drug development, yet most novel adaptive designs are difficult to understand, require complicated statistical modeling, demand complex computation, and need expensive infrastructure for implementation. The objective of this article is to introduce and review a class of novel adaptive designs, known as model-assisted designs, to remove this barrier and increase the use of novel adaptive designs. Model-assisted designs enjoy superior performance comparable to more complicated, model-based adaptive designs, but their decision rule can be pretabulated and included in the protocol-thus implemented as simply as the conventional designs. We review state-of-the-art model-assisted designs for phase I clinical trials for single-agent, drug-combination and late-onset toxicity scenarios. We also briefly introduce model-assisted designs for phase II trials to handle binary, coprimary endpoints and delayed response. Freely available user-friendly software and trial examples (trialdesign.org) facilitate the adoption of model-assisted designs.
© 2019 by American Society of Clinical Oncology.

Entities:  

Year:  2019        PMID: 32923856      PMCID: PMC7446379          DOI: 10.1200/PO.19.00032

Source DB:  PubMed          Journal:  JCO Precis Oncol        ISSN: 2473-4284


  46 in total

1.  Dose-finding with two agents in Phase I oncology trials.

Authors:  Peter F Thall; Randall E Millikan; Peter Mueller; Sang-Joon Lee
Journal:  Biometrics       Date:  2003-09       Impact factor: 2.571

2.  Bayesian optimal interval design for dose finding in drug-combination trials.

Authors:  Ruitao Lin; Guosheng Yin
Journal:  Stat Methods Med Res       Date:  2015-07-15       Impact factor: 3.021

3.  STEIN: A simple toxicity and efficacy interval design for seamless phase I/II clinical trials.

Authors:  Ruitao Lin; Guosheng Yin
Journal:  Stat Med       Date:  2017-08-07       Impact factor: 2.373

4.  BOP2: Bayesian optimal design for phase II clinical trials with simple and complex endpoints.

Authors:  Heng Zhou; J Jack Lee; Ying Yuan
Journal:  Stat Med       Date:  2017-06-07       Impact factor: 2.373

5.  Design and analysis of phase I clinical trials.

Authors:  B E Storer
Journal:  Biometrics       Date:  1989-09       Impact factor: 2.571

6.  Revised response criteria for malignant lymphoma.

Authors:  Bruce D Cheson; Beate Pfistner; Malik E Juweid; Randy D Gascoyne; Lena Specht; Sandra J Horning; Bertrand Coiffier; Richard I Fisher; Anton Hagenbeek; Emanuele Zucca; Steven T Rosen; Sigrid Stroobants; T Andrew Lister; Richard T Hoppe; Martin Dreyling; Kensei Tobinai; Julie M Vose; Joseph M Connors; Massimo Federico; Volker Diehl
Journal:  J Clin Oncol       Date:  2007-01-22       Impact factor: 44.544

7.  Continual reassessment method for partial ordering.

Authors:  Nolan A Wages; Mark R Conaway; John O'Quigley
Journal:  Biometrics       Date:  2011-03-01       Impact factor: 2.571

8.  Accelerated titration designs for phase I clinical trials in oncology.

Authors:  R Simon; B Freidlin; L Rubinstein; S G Arbuck; J Collins; M C Christian
Journal:  J Natl Cancer Inst       Date:  1997-08-06       Impact factor: 13.506

9.  A Bayesian dose-finding design for drug combination clinical trials based on the logistic model.

Authors:  Marie-Karelle Riviere; Ying Yuan; Frédéric Dubois; Sarah Zohar
Journal:  Pharm Stat       Date:  2014-05-15       Impact factor: 1.894

10.  A comprehensive comparison of the continual reassessment method to the standard 3 + 3 dose escalation scheme in Phase I dose-finding studies.

Authors:  Alexia Iasonos; Andrew S Wilton; Elyn R Riedel; Venkatraman E Seshan; David R Spriggs
Journal:  Clin Trials       Date:  2008       Impact factor: 2.486

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  6 in total

1.  BOIN12: Bayesian Optimal Interval Phase I/II Trial Design for Utility-Based Dose Finding in Immunotherapy and Targeted Therapies.

Authors:  Ruitao Lin; Yanhong Zhou; Fangrong Yan; Daniel Li; Ying Yuan
Journal:  JCO Precis Oncol       Date:  2020-11-16

2.  Lessons Learned From Implementing a Novel Bayesian Adaptive Dose-Finding Design in Advanced Pancreatic Cancer.

Authors:  Rebecca S S Tidwell; Peter F Thall; Ying Yuan
Journal:  JCO Precis Oncol       Date:  2021-11-10

Review 3.  BOIN: a novel Bayesian design platform to accelerate early phase brain tumor clinical trials.

Authors:  Ying Yuan; Jing Wu; Mark R Gilbert
Journal:  Neurooncol Pract       Date:  2021-06-11

4.  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

5.  BOIN Suite: A Software Platform to Design and Implement Novel Early-Phase Clinical Trials.

Authors:  Yanhong Zhou; Ruitao Lin; Ying-Wei Kuo; J Jack Lee; Ying Yuan
Journal:  JCO Clin Cancer Inform       Date:  2021-01

6.  Evaluation of Deviation From Planned Cohort Size and Operating Characteristics of Phase 1 Trials.

Authors:  Minjeong Park; Suyu Liu; Timothy Anthony Yap; Ying Yuan
Journal:  JAMA Netw Open       Date:  2021-02-01
  6 in total

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