Literature DB >> 28546227

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

Fangrong Yan1, Sumithra J Mandrekar2, Ying Yuan3.   

Abstract

The primary objective of phase I oncology trials is to find the MTD. The 3+3 design is easy to implement but performs poorly in finding the MTD. A newer design, such as the modified toxicity probability interval (mTPI) design, provides better accuracy to identify the MTD but tends to overdose patients. We propose the keyboard design, an intuitive Bayesian design that conducts dose escalation and de-escalation based on whether the strongest key, defined as the dosing interval that most likely contains the current dose, is below or above the target dosing interval. The keyboard design can be implemented in a simple way, similar to the traditional 3+3 design, but provides more flexibility for choosing the target toxicity rate and cohort size. Our simulation studies demonstrate that compared with the 3+3 design, the keyboard design has favorable operating characteristics in terms of identifying the MTD. Compared with the mTPI design, the keyboard design is safer, with a substantially lower risk of treating patients at overly toxic doses, and has the better precision to identify the MTD, thereby providing a useful upgrade to the mTPI design. Software freely available at http://www.trialdesign.org facilitates the application of the keyboard design. Clin Cancer Res; 23(15); 3994-4003. ©2017 AACRSee related commentary by Paoletti et al., p. 3977. ©2017 American Association for Cancer Research.

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Year:  2017        PMID: 28546227      PMCID: PMC5554436          DOI: 10.1158/1078-0432.CCR-17-0220

Source DB:  PubMed          Journal:  Clin Cancer Res        ISSN: 1078-0432            Impact factor:   12.531


  21 in total

1.  Translation of innovative designs into phase I trials.

Authors:  André Rogatko; David Schoeneck; William Jonas; Mourad Tighiouart; Fadlo R Khuri; Alan Porter
Journal:  J Clin Oncol       Date:  2007-11-01       Impact factor: 44.544

2.  Modified toxicity probability interval design: a safer and more reliable method than the 3 + 3 design for practical phase I trials.

Authors:  Yuan Ji; Sue-Jane Wang
Journal:  J Clin Oncol       Date:  2013-04-08       Impact factor: 44.544

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

4.  A practical Bayesian design to identify the maximum tolerated dose contour for drug combination trials.

Authors:  Liangcai Zhang; Ying Yuan
Journal:  Stat Med       Date:  2016-08-31       Impact factor: 2.373

5.  A comparison of two phase I trial designs.

Authors:  E L Korn; D Midthune; T T Chen; L V Rubinstein; M C Christian; R M Simon
Journal:  Stat Med       Date:  1994-09-30       Impact factor: 2.373

6.  Sequential designs for phase I clinical trials with late-onset toxicities.

Authors:  Y K Cheung; R Chappell
Journal:  Biometrics       Date:  2000-12       Impact factor: 2.571

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

8.  BAYESIAN DATA AUGMENTATION DOSE FINDING WITH CONTINUAL REASSESSMENT METHOD AND DELAYED TOXICITY.

Authors:  Suyu Liu; Guosheng Yin; Ying Yuan
Journal:  Ann Appl Stat       Date:  2013-12-01       Impact factor: 2.083

9.  Efficiency of new dose escalation designs in dose-finding phase I trials of molecularly targeted agents.

Authors:  Christophe Le Tourneau; Hui K Gan; Albiruni R A Razak; Xavier Paoletti
Journal:  PLoS One       Date:  2012-12-12       Impact factor: 3.240

Review 10.  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

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  27 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.  Coherence principles in interval-based dose finding.

Authors:  Nolan A Wages; Alexia Iasonos; John O'Quigley; Mark R Conaway
Journal:  Pharm Stat       Date:  2019-11-06       Impact factor: 1.894

Review 3.  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

4.  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

5.  On the relative efficiency of model-assisted designs: a conditional approach.

Authors:  Ruitao Lin; Ying Yuan
Journal:  J Biopharm Stat       Date:  2019-06-29       Impact factor: 1.051

6.  Evaluation of irrational dose assignment definitions using the continual reassessment method.

Authors:  Nolan A Wages; Evan Bagley
Journal:  Clin Trials       Date:  2019-09-23       Impact factor: 2.486

7.  Time-to-Event Bayesian Optimal Interval Design to Accelerate Phase I Trials.

Authors:  Ying Yuan; Ruitao Lin; Daniel Li; Lei Nie; Katherine E Warren
Journal:  Clin Cancer Res       Date:  2018-05-16       Impact factor: 12.531

8.  Revisiting isotonic phase I design in the era of model-assisted dose-finding.

Authors:  Nolan A Wages; Mark R Conaway
Journal:  Clin Trials       Date:  2018-08-13       Impact factor: 2.486

9.  Time-to-event model-assisted designs for dose-finding trials with delayed toxicity.

Authors:  Ruitao Lin; Ying Yuan
Journal:  Biostatistics       Date:  2020-10-01       Impact factor: 5.899

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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