Embracing model-based designs for dose-finding trials
File(s)bjc2017186a.pdf (823.02 KB)
Published version
Author(s)
Type
Journal Article
Abstract
Background:
Dose-finding trials are essential to drug development as they establish recommended doses for later-phase testing. We aim to motivate wider use of model-based designs for dose finding, such as the continual reassessment method (CRM).
Methods:
We carried out a literature review of dose-finding designs and conducted a survey to identify perceived barriers to their implementation.
Results:
We describe the benefits of model-based designs (flexibility, superior operating characteristics, extended scope), their current uptake, and existing resources. The most prominent barriers to implementation of a model-based design were lack of suitable training, chief investigators’ preference for algorithm-based designs (e.g., 3+3), and limited resources for study design before funding. We use a real-world example to illustrate how these barriers can be overcome.
Conclusions:
There is overwhelming evidence for the benefits of CRM. Many leading pharmaceutical companies routinely implement model-based designs. Our analysis identified barriers for academic statisticians and clinical academics in mirroring the progress industry has made in trial design. Unified support from funders, regulators, and journal editors could result in more accurate doses for later-phase testing, and increase the efficiency and success of clinical drug development. We give recommendations for increasing the uptake of model-based designs for dose-finding trials in academia.
Dose-finding trials are essential to drug development as they establish recommended doses for later-phase testing. We aim to motivate wider use of model-based designs for dose finding, such as the continual reassessment method (CRM).
Methods:
We carried out a literature review of dose-finding designs and conducted a survey to identify perceived barriers to their implementation.
Results:
We describe the benefits of model-based designs (flexibility, superior operating characteristics, extended scope), their current uptake, and existing resources. The most prominent barriers to implementation of a model-based design were lack of suitable training, chief investigators’ preference for algorithm-based designs (e.g., 3+3), and limited resources for study design before funding. We use a real-world example to illustrate how these barriers can be overcome.
Conclusions:
There is overwhelming evidence for the benefits of CRM. Many leading pharmaceutical companies routinely implement model-based designs. Our analysis identified barriers for academic statisticians and clinical academics in mirroring the progress industry has made in trial design. Unified support from funders, regulators, and journal editors could result in more accurate doses for later-phase testing, and increase the efficiency and success of clinical drug development. We give recommendations for increasing the uptake of model-based designs for dose-finding trials in academia.
Date Issued
2017-06-29
Date Acceptance
2017-05-15
Citation
British Journal of Cancer, 2017, 117, pp.332-339
ISSN
1532-1827
Publisher
Cancer Research UK
Start Page
332
End Page
339
Journal / Book Title
British Journal of Cancer
Volume
117
Copyright Statement
© The Author(s) named above 2017. This work is licensed under the Creative Commons
Attribution 4.0 International License. To view a copy
of this license, visit http://creativecommons.org/licenses/by/4.0/
Attribution 4.0 International License. To view a copy
of this license, visit http://creativecommons.org/licenses/by/4.0/
License URL
Subjects
Science & Technology
Life Sciences & Biomedicine
Oncology
model-based design
dose-finding trials
phase I
CRM
3+3
CONTINUAL REASSESSMENT METHOD
I CLINICAL-TRIALS
CONFIRMATORY TRIALS
ADAPTIVE DESIGNS
KEY STAKEHOLDERS
PHASE
CANCER
ESCALATION
TOXICITIES
ONCOLOGY
Attitude
Clinical Trials, Phase I as Topic
Dose-Response Relationship, Drug
Humans
Maximum Tolerated Dose
Models, Statistical
Professional Competence
Research Personnel
Software
Surveys and Questionnaires
Time Factors
1112 Oncology And Carcinogenesis
Oncology & Carcinogenesis
Publication Status
Published