Literature DB >> 33595664

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

Minjeong Park1, Suyu Liu2, Timothy Anthony Yap3,4,5, Ying Yuan2.   

Abstract

Importance: The cohort size of phase 1 clinical trials and thus the timing of the interim decisions are typically prespecified in the trial protocol. During trial implementation, however, the cohort size often deviates from the planned one, which shifts the schedule of the interims. Despite its pervasiveness in phase 1 trials, the association of cohort size deviation with the operating characteristics of these trials is not clear. Objective: To explore the association between cohort size deviation and the operating characteristics of phase 1 clinical trials. Design, Setting, and Participants: In this cross-sectional simulation study, a review was conducted of 102 phase 1 dose-escalation trials published between January 2017 and May 2018 in 3 peer-reviewed journals (Journal of Clinical Oncology, Clinical Cancer Research, and Cancer). After exclusion of studies that did not report the cohort size, 45 trials remained for analysis. Based on the analysis results, a simulation study was performed to systematically investigate the association of cohort size deviation with the operating characteristics of the trials. Main Outcomes and Measures: The prevalence of cohort size deviation and the percentage of correct selection of the maximum tolerated dose.
Results: Of the 45 reviewed trials, 10 (22.2%) adhered strictly to the planned cohort size. The simulation study showed that when cohort size deviation was random, it had little association with the performance of novel model-based and model-assisted designs (mean reduction in the percentage of correct selection of the maximum tolerated dose was 0.87 percentage point for the continual reassessment method and 0.84 percentage point for the bayesian optimal interval design). When the cohort size deviation was informative and made based on the observed data on toxicity (eg, if dose-limiting toxicity was observed, the size of the next or current cohort was reduced or expanded), the variation of the design performance increased. The range of the change in the percentage of correct selection was -3.7 to 1.3 percentage points for the continual reassessment method and -4.5 to 0 percentage points for the bayesian optimal interval design. Conclusions and Relevance: The findings suggest that when novel phase 1 clinical trial designs are used, some cohort size deviation is acceptable and has little association with the performance of the designs. These deviations may be used by expert investigators to properly interpret the data, ensure safety, and leverage flexibility in the protocol.

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Year:  2021        PMID: 33595664      PMCID: PMC7890531          DOI: 10.1001/jamanetworkopen.2020.37563

Source DB:  PubMed          Journal:  JAMA Netw Open        ISSN: 2574-3805


  9 in total

1.  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

2.  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

3.  Bayesian Optimal Interval Design: A Simple and Well-Performing Design for Phase I Oncology Trials.

Authors:  Ying Yuan; Kenneth R Hess; Susan G Hilsenbeck; Mark R Gilbert
Journal:  Clin Cancer Res       Date:  2016-07-12       Impact factor: 12.531

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.  Accuracy, Safety, and Reliability of Novel Phase I Trial Designs.

Authors:  Heng Zhou; Ying Yuan; Lei Nie
Journal:  Clin Cancer Res       Date:  2018-04-16       Impact factor: 12.531

6.  Keyboard: A Novel Bayesian Toxicity Probability Interval Design for Phase I Clinical Trials.

Authors:  Fangrong Yan; Sumithra J Mandrekar; Ying Yuan
Journal:  Clin Cancer Res       Date:  2017-05-25       Impact factor: 12.531

7.  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

Review 8.  Dose escalation methods in phase I cancer clinical trials.

Authors:  Christophe Le Tourneau; J Jack Lee; Lillian L Siu
Journal:  J Natl Cancer Inst       Date:  2009-05-12       Impact factor: 13.506

9.  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

  9 in total
  2 in total

Review 1.  An overview of the BOIN design and its current extensions for novel early-phase oncology trials.

Authors:  Revathi Ananthakrishnan; Ruitao Lin; Chunsheng He; Yanping Chen; Daniel Li; Michael LaValley
Journal:  Contemp Clin Trials Commun       Date:  2022-06-13

Review 2.  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
  2 in total

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