Literature DB >> 24464821

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

M-K Riviere1, F Dubois, S Zohar.   

Abstract

The aim of phase I combination dose-finding studies in oncology is to estimate one or several maximum tolerated doses (MTDs) from a set of available dose levels of two or more agents. Combining several agents can indeed increase the overall anti-tumor action but at the same time also increase the toxicity. It is, however, unreasonable to assume the same dose-toxicity relationship for the combination as for the simple addition of each single agent because of a potential antagonist or synergistic effect. Therefore, using single-agent dose-finding methods for combination therapies is not appropriate. In recent years, several authors have proposed novel dose-finding designs for combination studies, which use either algorithm-based or model-based methods. The aim of our work was to compare, via a simulation study, six dose-finding methods for combinations proposed in recent years. We chose eight scenarios that differ in terms of the number and location of the true MTD(s) in the combination space. We then compared the performance of each design in terms of correct combination selection, patient allocation, and mean number of observed toxicities during the trials. Our results showed that the model-based methods performed better than the algorithm-based ones. However, none of the compared model-based designs gave consistently better results than the others.
Copyright © 2014 John Wiley & Sons, Ltd.

Entities:  

Keywords:  Bayesian inference; cytotoxic; dose-finding studies; drug combination; oncology; phase I trial

Mesh:

Substances:

Year:  2014        PMID: 24464821     DOI: 10.1002/sim.6094

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


  11 in total

Review 1.  The changing landscape of phase I trials in oncology.

Authors:  Kit Man Wong; Anna Capasso; S Gail Eckhardt
Journal:  Nat Rev Clin Oncol       Date:  2015-11-10       Impact factor: 66.675

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

Authors:  Akihiro Hirakawa; Nolan A Wages; Hiroyuki Sato; Shigeyuki Matsui
Journal:  Stat Med       Date:  2015-05-13       Impact factor: 2.373

3.  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

Review 4.  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

5.  Comments on 'competing designs for drug combination in phase I dose-finding clinical trials' by M-K. Riviere, F. Dubois, S. Zohar.

Authors:  Nolan A Wages
Journal:  Stat Med       Date:  2015-01-15       Impact factor: 2.373

6.  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

7.  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

8.  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

9.  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

10.  Adding flexibility to clinical trial designs: an example-based guide to the practical use of adaptive designs.

Authors:  Thomas Burnett; Pavel Mozgunov; Philip Pallmann; Sofia S Villar; Graham M Wheeler; Thomas Jaki
Journal:  BMC Med       Date:  2020-11-19       Impact factor: 8.775

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