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In the News
ICON experts frequently author or contribute to industry trade press.
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How real world evidence is impacting rare disease drug development
Will Maier, VP of Rare Diseases, shares how a more patient-focused approach to trials is leading to alternatives for randomisation.
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The cross-over of statistical thinking and practices: A pandemic catalyst
This article focuses on what others could learn from good statistical practices in drug development and considers areas where drug development could benefit from some fresh thinking
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Improving early phase oncology clinical trial design: A case study
Tim Clark, VP Drug Development Solutions and Alan Phillips, Sr. Director Biostatistics present a case study on the first in human Bayesian Optimal Interval design.
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ICON wins ‘Company of the Year’ at the Business & Finance Awards 2022
Steve Culter gives insight into ICON as a company along with his thoughts on winning this award.
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What Biotech and Pharma need to consider about decentralised trials
Discuss key considerations when implementing decentralised clinical trials, in addition to the success factors that lead to more patient-centric trials and increased patient recruitment, engagement and retention.
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The surge of digital health technologies modernising clinical research
Dr. Isaac R. Rodriguez-Chavez outlines the key considerations when developing digital health technologies.
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In the News
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Cell and gene therapy specific market authorisation guidances
Brandon Fletcher, Principal, Cell and Gene Therapy, offers his insights on the key changes in regulatory guidance documents released by the US Food and Drug Agency.
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Improving early phase oncology clinical trial design using Bayesian-based BOIN and BOP2 designs
Tim Clark, VP Drug Development Solutions, Martin Lachs, VP Project Management Oncology & Cell Therapeutics and Alan Phillips, Sr. Director Biostatistics, offer insights on selecting and implementing the appropriate model-based or model-assisted design for early phase oncology trials.
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Evaluating similarities and differences between machine learning and traditional statistical modeling in healthcare analytics
ICON experts Michele Bennett, Ewa J. Kleczyk, Karin Hayes and Rajesh Mehta explain why data scientists and analysts should not necessarily have to choose between either machine learning or statistical modeling as a mutually exclusive decision tree.