Data is the most crucial asset in any clinical trial and is used to ultimately drive the decision-making process related to the development candidate. Therefore, for any sponsor, paying close attention to the data management aspects of clinical operations should be paramount. When implementing data management for your trial it is critical to plan ahead and fully understand all the steps and activities involved.
While medical science behind immuno oncology (IO) treatments is fascinating and expanding at a rapid pace, so too are the statistical challenges posed by the development of these agents. Methods for the design and analysis of IO trials are an emerging area of clinical research where statisticians are playing an important role in the development and application of analytical approaches.
Statisticians and clinicians do tend to talk a different language, both using very different technical terminology. Statisticians sometimes struggle to understand the technical language that clinicians use, and clinicians of course sometimes struggle to understand the terminology of statisticians. Without the right input from all parties you risk reaching the end of the project and realizing a mistake was made at the outset.
The shift from on-site monitoring to remote monitoring has given the data manager increased responsibility for looking at the data in real time and to allow decisions to be made on a site and patient level on an ongoing basis. This article discusses what makes a great data manager in today’s drug development environment.
This 2018 survey report based on data from over 140 industry respondents reveals how the evolution of data science could lead to a revolution of real-world data being accepted as robust evidence for the regulatory approval of new drugs.
Cytel data scientists apply advanced statistical techniques including predictive modeling of biological processes and drug interactions to unlock the potential of Big Data. The team supports biomarker discovery and diagnostic test development based on biomedical signals and images, and real world evidence analysis.