Literature DB >> 34320376

Operating characteristics are needed to properly evaluate the scientific validity of phase I protocols.

Nolan A Wages1, Bethany Jablonski Horton2, Mark R Conaway2, Gina R Petroni2.   

Abstract

PURPOSE: Operating characteristics for proposed clinical trial designs provide insight into performance regarding safety and accuracy, allowing the study team and review entities to determine the design's suitability to achieve the study's proposed objectives. Advances in cancer therapeutics have augmented the needs of early phase clinical trial design. Additionally, advances in research on early-phase trial design have led to the availability of a wide range of methods that show vast improvement over outdated approaches.
METHODS: Three trials utilizing variations of the 3 + 3 decision rule are discussed. The protocols lacked detail, including operating characteristics and guidance for decision-making that deviated from the 3 + 3 decision rule and MTD determination. We provide a discussion of the statistical issues associated with each design and operating characteristics for the proposed design compared to alternatives better suited to achieve the aims of each trial.
RESULTS: Our results illustrate how operating characteristics inform a design's safety and accuracy. Operating characteristics can unmask poor behavior, such as a high percentage of particiapnts exposed to overly toxic doses, a low probability of correctly identifying the MTD, and inappropriate early study termination.
CONCLUSION: Selection of early-phase trial design has significant implications on a trial's ability to meet its objectives. Operating characteristics are a necessary component in the design and review of a protocol, determining if the study's objectives can be achieved and documenting the study's scientific validity. Continued use of outdated approaches due to historical acceptance hinders scientific rigor and the effort to move effective agents through the drug development process.
Copyright © 2021 Elsevier Inc. All rights reserved.

Entities:  

Keywords:  Clinical trials; Operating characteristics; Phase I; Protocol; Scientific validity

Mesh:

Year:  2021        PMID: 34320376      PMCID: PMC8453118          DOI: 10.1016/j.cct.2021.106517

Source DB:  PubMed          Journal:  Contemp Clin Trials        ISSN: 1551-7144            Impact factor:   2.261


  36 in total

1.  A model-based approach in the estimation of the maximum tolerated dose in phase I cancer clinical trials.

Authors:  Weili He; Jun Liu; Bruce Binkowitz; Hui Quan
Journal:  Stat Med       Date:  2006-06-30       Impact factor: 2.373

2.  Continuous toxicity monitoring in phase II trials in oncology.

Authors:  Anastasia Ivanova; Bahjat F Qaqish; Michael J Schell
Journal:  Biometrics       Date:  2005-06       Impact factor: 2.571

3.  Range and trend of expected toxicity level (ETL) in standard A + B designs: a report from the Children's Oncology Group.

Authors:  Zhengjia Chen; Mark D Krailo; Junfeng Sun; Stanley P Azen
Journal:  Contemp Clin Trials       Date:  2008-10-29       Impact factor: 2.226

Review 4.  Adaptive dose-finding studies: a review of model-guided phase I clinical trials.

Authors:  Alexia Iasonos; John O'Quigley
Journal:  J Clin Oncol       Date:  2014-06-30       Impact factor: 44.544

5.  Dose Transition Pathways: The Missing Link Between Complex Dose-Finding Designs and Simple Decision-Making.

Authors:  Christina Yap; Lucinda J Billingham; Ying Kuen Cheung; Charlie Craddock; John O'Quigley
Journal:  Clin Cancer Res       Date:  2017-07-21       Impact factor: 12.531

6.  Rendering the 3 + 3 Design to Rest: More Efficient Approaches to Oncology Dose-Finding Trials in the Era of Targeted Therapy.

Authors:  Lei Nie; Eric H Rubin; Nitin Mehrotra; José Pinheiro; Laura L Fernandes; Amit Roy; Stuart Bailey; Dinesh P de Alwis
Journal:  Clin Cancer Res       Date:  2016-06-01       Impact factor: 12.531

7.  Phase I-II clinical trial design: a state-of-the-art paradigm for dose finding.

Authors:  F Yan; P F Thall; K H Lu; M R Gilbert; Y Yuan
Journal:  Ann Oncol       Date:  2018-03-01       Impact factor: 32.976

8.  Model calibration in the continual reassessment method.

Authors:  Shing M Lee
Journal:  Clin Trials       Date:  2009-06       Impact factor: 2.486

9.  Designing and evaluating dose-escalation studies made easy: The MoDEsT web app.

Authors:  Philip Pallmann; Fang Wan; Adrian P Mander; Graham M Wheeler; Christina Yap; Sally Clive; Lisa V Hampson; Thomas Jaki
Journal:  Clin Trials       Date:  2019-12-19       Impact factor: 2.486

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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  1 in total

1.  Bayesian Design for Identifying Cohort-Specific Optimal Dose Combinations Based on Multiple Endpoints: Application to a Phase I Trial in Non-Small Cell Lung Cancer.

Authors:  Bethany Jablonski Horton; Nolan A Wages; Ryan D Gentzler
Journal:  Int J Environ Res Public Health       Date:  2021-10-30       Impact factor: 3.390

  1 in total

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