Literature DB >> 28786138

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

Ruitao Lin1, Guosheng Yin2.   

Abstract

Seamless phase I/II dose-finding trials are attracting increasing attention nowadays in early-phase drug development for oncology. Most existing phase I/II dose-finding methods use sophisticated yet untestable models to quantify dose-toxicity and dose-efficacy relationships, which always renders them difficult to implement in practice. To simplify the practical implementation, we extend the Bayesian optimal interval design from maximum tolerated dose finding to optimal biological dose finding in phase I/II trials. In particular, optimized intervals for toxicity and efficacy are respectively derived by minimizing probabilities of incorrect classifications. If the pair of observed toxicity and efficacy probabilities at the current dose is located inside the promising region, we retain the current dose; if the observed probabilities are outside of the promising region, we propose an allocation rule by maximizing the posterior probability that the response rate of the next dose falls inside a prespecified efficacy probability interval while still controlling the level of toxicity. The proposed interval design is model-free, thus is suitable for various dose-response relationships. We conduct extensive simulation studies to demonstrate the small- and large-sample performance of the proposed method under various scenarios. Compared to existing phase I/II dose-finding designs, not only is our interval design easy to implement in practice, but it also possesses desirable and robust operating characteristics.
Copyright © 2017 John Wiley & Sons, Ltd.

Entities:  

Keywords:  Bayesian posterior probability; adaptive design; dose finding; interval design; optimal biological dose; phase I/II trial

Mesh:

Substances:

Year:  2017        PMID: 28786138     DOI: 10.1002/sim.7428

Source DB:  PubMed          Journal:  Stat Med        ISSN: 0277-6715            Impact factor:   2.373


  11 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

Review 2.  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

Review 3.  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

4.  TITE-BOIN12: A Bayesian phase I/II trial design to find the optimal biological dose with late-onset toxicity and efficacy.

Authors:  Yanhong Zhou; Ruitao Lin; J Jack Lee; Daniel Li; Li Wang; Ruobing Li; Ying Yuan
Journal:  Stat Med       Date:  2022-01-31       Impact factor: 2.497

Review 5.  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

6.  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

7.  CFO: Calibration-free odds design for phase I/II clinical trials.

Authors:  Huaqing Jin; Guosheng Yin
Journal:  Stat Methods Med Res       Date:  2022-03-03       Impact factor: 2.494

8.  Interval design to identify the optimal biological dose for immunotherapy.

Authors:  Yeonhee Park
Journal:  Contemp Clin Trials Commun       Date:  2022-09-24

9.  2D (2 Dimensional) TEQR design for Determining the optimal Dose for safety and efficacy.

Authors:  Revathi Ananthakrishnan; Stephanie Green; Daniel Li; Michael LaValley
Journal:  Contemp Clin Trials Commun       Date:  2019-10-12

10.  SCI: A Bayesian adaptive phase I/II dose-finding design accounting for semi-competing risks outcomes for immunotherapy trials.

Authors:  Yifei Zhang; Beibei Guo; Sha Cao; Chi Zhang; Yong Zang
Journal:  Pharm Stat       Date:  2022-03-24       Impact factor: 1.234

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