Literature DB >> 25974405

A comparative study of adaptive dose-finding designs for phase I oncology trials of combination therapies.

Akihiro Hirakawa1, Nolan A Wages2, Hiroyuki Sato3, Shigeyuki Matsui4.   

Abstract

Little is known about the relative performance of competing model-based dose-finding methods for combination phase I trials. In this study, we focused on five model-based dose-finding methods that have been recently developed. We compared the recommendation rates for true maximum-tolerated dose combinations (MTDCs) and over-dose combinations among these methods under 16 scenarios for 3 × 3, 4 × 4, 2 × 4, and 3 × 5 dose combination matrices. We found that performance of the model-based dose-finding methods varied depending on (1) whether the dose combination matrix is square or not; (2) whether the true MTDCs exist within the same group along the diagonals of the dose combination matrix; and (3) the number of true MTDCs. We discuss the details of the operating characteristics and the advantages and disadvantages of the five methods compared.
Copyright © 2015 John Wiley & Sons, Ltd.

Entities:  

Keywords:  combination of two agents; comparative study; dose-finding method; oncology; phase I trial

Mesh:

Year:  2015        PMID: 25974405      PMCID: PMC4806394          DOI: 10.1002/sim.6533

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


  28 in total

1.  Continual reassessment methods in phase I trials of the combination of two drugs in oncology.

Authors:  A Kramar; A Lebecq; E Candalh
Journal:  Stat Med       Date:  1999-07-30       Impact factor: 2.373

2.  Designs for single- or multiple-agent phase I trials.

Authors:  Mark R Conaway; Stephanie Dunbar; Shyamal D Peddada
Journal:  Biometrics       Date:  2004-09       Impact factor: 2.571

3.  Two-dimensional dose finding in discrete dose space.

Authors:  Kai Wang; Anastasia Ivanova
Journal:  Biometrics       Date:  2005-03       Impact factor: 2.571

4.  Design issues in dose-finding Phase I trials for combinations of two agents.

Authors:  Shenghua Kelly Fan; Alan P Venook; Ying Lu
Journal:  J Biopharm Stat       Date:  2009       Impact factor: 1.051

5.  A dose-finding approach based on shrunken predictive probability for combinations of two agents in phase I trials.

Authors:  Akihiro Hirakawa; Chikuma Hamada; Shigeyuki Matsui
Journal:  Stat Med       Date:  2013-05-06       Impact factor: 2.373

6.  A hierarchical Bayesian design for phase I trials of novel combinations of cancer therapeutic agents.

Authors:  Thomas M Braun; Shufang Wang
Journal:  Biometrics       Date:  2010-09       Impact factor: 2.571

7.  Phase I study of neratinib in combination with temsirolimus in patients with human epidermal growth factor receptor 2-dependent and other solid tumors.

Authors:  Leena Gandhi; Rastislav Bahleda; Sara M Tolaney; Eunice L Kwak; James M Cleary; Shuchi S Pandya; Antoine Hollebecque; Richat Abbas; Revathi Ananthakrishnan; Anna Berkenblit; Mizue Krygowski; Yali Liang; Kathleen W Turnbull; Geoffrey I Shapiro; Jean-Charles Soria
Journal:  J Clin Oncol       Date:  2013-12-09       Impact factor: 44.544

8.  Competing designs for drug combination in phase I dose-finding clinical trials.

Authors:  M-K Riviere; F Dubois; S Zohar
Journal:  Stat Med       Date:  2014-01-27       Impact factor: 2.373

Review 9.  Adaptive designs for dual-agent phase I dose-escalation studies.

Authors:  Jennifer A Harrington; Graham M Wheeler; Michael J Sweeting; Adrian P Mander; Duncan I Jodrell
Journal:  Nat Rev Clin Oncol       Date:  2013-03-19       Impact factor: 66.675

10.  A product of independent beta probabilities dose escalation design for dual-agent phase I trials.

Authors:  Adrian P Mander; Michael J Sweeting
Journal:  Stat Med       Date:  2015-01-29       Impact factor: 2.373

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  11 in total

1.  CRM2DIM: A SAS macro for implementing the dual-agent Bayesian continual reassessment method.

Authors:  Mohamed Amine Bayar; Anastasia Ivanova; Gwénaël Le Teuff
Journal:  Comput Methods Programs Biomed       Date:  2019-05-06       Impact factor: 5.428

2.  AAA: triple adaptive Bayesian designs for the identification of optimal dose combinations in dual-agent dose finding trials.

Authors:  Jiaying Lyu; Yuan Ji; Naiqing Zhao; Daniel V T Catenacci
Journal:  J R Stat Soc Ser C Appl Stat       Date:  2018-06-13       Impact factor: 1.864

Review 3.  Practical designs for Phase I combination studies in oncology.

Authors:  Nolan A Wages; Anastasia Ivanova; Olga Marchenko
Journal:  J Biopharm Stat       Date:  2016       Impact factor: 1.051

4.  The Impact of Early-Phase Trial Design in the Drug Development Process.

Authors:  Mark R Conaway; Gina R Petroni
Journal:  Clin Cancer Res       Date:  2018-10-16       Impact factor: 12.531

5.  Designs for phase I trials in ordered groups.

Authors:  Mark R Conaway; Nolan A Wages
Journal:  Stat Med       Date:  2016-09-14       Impact factor: 2.373

6.  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

7.  A surface-free design for phase I dual-agent combination trials.

Authors:  Pavel Mozgunov; Mauro Gasparini; Thomas Jaki
Journal:  Stat Methods Med Res       Date:  2020-04-27       Impact factor: 3.021

8.  A benchmark for dose-finding studies with unknown ordering.

Authors:  Pavel Mozgunov; Xavier Paoletti; Thomas Jaki
Journal:  Biostatistics       Date:  2022-07-18       Impact factor: 5.279

9.  Using an Interaction Parameter in Model-Based Phase I Trials for Combination Treatments? A Simulation Study.

Authors:  Pavel Mozgunov; Rochelle Knight; Helen Barnett; Thomas Jaki
Journal:  Int J Environ Res Public Health       Date:  2021-01-05       Impact factor: 3.390

10.  How to design a dose-finding study on combined agents: Choice of design and development of R functions.

Authors:  Monia Ezzalfani
Journal:  PLoS One       Date:  2019-11-11       Impact factor: 3.240

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