Literature DB >> 32939800

A simulation-free approach to assessing the performance of the continual reassessment method.

Thomas M Braun1.   

Abstract

The continual reassessment method (CRM) is an adaptive design for Phase I trials whose operating characteristics, including appropriate sample size, probability of correctly identifying the maximum tolerated dose, and the expected proportion of participants assigned to each dose, can only be determined via simulation. The actual time to determine a final "best" design can take several hours or days, depending on the number of scenarios that are examined. The computational cost increases as the kernel of the one-parameter CRM design is expanded to other settings, including additional parameters, monitoring of both toxicity and efficacy, and studies of combinations of two agents. For a given vector of true DLT probabilities, we have developed an approach that replaces a simulation study of thousands of hypothetical trials with a single simulation. Our approach, which is founded on the consistency of the CRM, very accurately reflects the results produced by the simulation study, but does so in a fraction of time required by the simulation study. Relative to traditional simulations, we extensively examine how our method is able to assess the operating characteristics of a CRM design for a hypothetical trial whose characteristics are based upon a previously published Phase I trial. We also provide a metric of nonconsistency and demonstrate that although nonconsistency can impact the operating characteristics of our method, the degree of over- or under-estimation is unpredictable. As a solution, we provide an algorithm for maintaining the consistency of a chosen CRM design so that our method is applicable for any trial.
© 2020 John Wiley & Sons, Ltd.

Entities:  

Keywords:  Bayesian methods; Phase I trial; adaptive clinical trial; consistency; dose-finding trial; nonparametric optimal design

Mesh:

Year:  2020        PMID: 32939800      PMCID: PMC9062987          DOI: 10.1002/sim.8746

Source DB:  PubMed          Journal:  Stat Med        ISSN: 0277-6715            Impact factor:   2.497


  16 in total

1.  Non-parametric optimal design in dose finding studies.

Authors:  John O'Quigley; Xavier Paoletti; Jean Maccario
Journal:  Biostatistics       Date:  2002-03       Impact factor: 5.899

2.  A simple technique to evaluate model sensitivity in the continual reassessment method.

Authors:  Ying Kuen Cheung; Rick Chappell
Journal:  Biometrics       Date:  2002-09       Impact factor: 2.571

3.  Dose-finding designs: the role of convergence properties.

Authors:  Assaf P Oron; David Azriel; Peter D Hoff
Journal:  Int J Biostat       Date:  2011-10-27       Impact factor: 0.968

4.  The continual reassessment method for dose-finding studies: a tutorial.

Authors:  Elizabeth Garrett-Mayer
Journal:  Clin Trials       Date:  2006       Impact factor: 2.486

5.  Critical aspects of the Bayesian approach to phase I cancer trials.

Authors:  Beat Neuenschwander; Michael Branson; Thomas Gsponer
Journal:  Stat Med       Date:  2008-06-15       Impact factor: 2.373

6.  Continual reassessment method: a practical design for phase 1 clinical trials in cancer.

Authors:  J O'Quigley; M Pepe; L Fisher
Journal:  Biometrics       Date:  1990-03       Impact factor: 2.571

7.  A Generalized Continual Reassessment Method for Two-Agent Phase I Trials.

Authors:  Thomas M Braun; Nan Jia
Journal:  Stat Biopharm Res       Date:  2013-01-01       Impact factor: 1.452

8.  Cancer phase I clinical trials: efficient dose escalation with overdose control.

Authors:  J Babb; A Rogatko; S Zacks
Journal:  Stat Med       Date:  1998-05-30       Impact factor: 2.373

9.  A web tool for designing and conducting phase I trials using the continual reassessment method.

Authors:  Nolan A Wages; Gina R Petroni
Journal:  BMC Cancer       Date:  2018-02-05       Impact factor: 4.430

10.  How to design a dose-finding study using the continual reassessment method.

Authors:  Graham M Wheeler; Adrian P Mander; Alun Bedding; Kristian Brock; Victoria Cornelius; Andrew P Grieve; Thomas Jaki; Sharon B Love; Lang'o Odondi; Christopher J Weir; Christina Yap; Simon J Bond
Journal:  BMC Med Res Methodol       Date:  2019-01-18       Impact factor: 4.615

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