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Challenges in implementing model-based phase I designs in a grant-funded clinical trials unit

Overview of attention for article published in Trials, December 2017
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Title
Challenges in implementing model-based phase I designs in a grant-funded clinical trials unit
Published in
Trials, December 2017
DOI 10.1186/s13063-017-2389-2
Pubmed ID
Authors

Eleni Frangou, Jane Holmes, Sharon Love, Naomi McGregor, Maria Hawkins

Abstract

For a clinical trials unit to run its first model-based, phase I trial, the statistician, chief investigator, and trial manager must all acquire a new set of skills. These trials also require a different approach to funding and data collection. From the statisticians' viewpoint, we highlight what is needed to move from running rule-based, early-phase trials to running a model-based phase I study as we experienced it in our trials unit located in the United Kingdom. Our example is CHARIOT, a dose-finding trial using the time-to-event continual reassessment method. It consists of three stages and aims to discover the maximum tolerated dose of the combination of radiotherapy, chemotherapy, and the ataxia telangiectasia mutated Rad3-related inhibitor M6620 (previously known as VX-970) in patients with oesophageal cancer. We present the challenges we faced in designing this trial and how we overcame them as a way of demystifying the conduct of a model-based trial in a grant-funded clinical trials unit. Although we appreciate that undertaking model-based trials requires additional time and effort, they are feasible to implement and, once suitable tools such as guiding publications and document templates become available, the design and set-up process will be easier and more efficient.

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The data shown below were compiled from readership statistics for 22 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 22 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 3 14%
Student > Master 3 14%
Lecturer 2 9%
Other 2 9%
Student > Ph. D. Student 2 9%
Other 4 18%
Unknown 6 27%
Readers by discipline Count As %
Nursing and Health Professions 4 18%
Environmental Science 1 5%
Biochemistry, Genetics and Molecular Biology 1 5%
Mathematics 1 5%
Unspecified 1 5%
Other 4 18%
Unknown 10 45%