With the rise in digital technologies, there has been an explosion in the volume and type of data sources we can obtain. However, new data sources bring inherent challenges to be overcome including lack of standardization, missing data, and variation in quality. Learn how data science and real-world evidence groups have helped clients apply advanced analytical techniques to large, complex historical, or real-world data sets to improve decision making, accelerate development pathways, and enhance the probability of success.
The right design and the right data ultimately lead to the right decisions, so obtaining fit-for-purpose data, collected based on what your protocol is looking for, is vital. However, there are several data pressure points facing oncology drug developers that need specialized expertise and processes to handle. This article presents some key aspects for consideration in order to smooth data collection and analysis.
In this Q&A with Laura Flight, National Institute for Health Research (NIHR) doctoral fellow, we take a deep dive into the objectives of her recent paper A Review of Clinical Trials With an Adaptive Design and Health Economic Analysis. Learn more about the next steps for promoting better understanding in this area.
A recurring question we get from clients is whether it is worth adopting data standards such as CDISC in the early phase of their drug development, and if it is worth spending more to produce SDTM and ADaM packages at an early stage. Learn more about why this could be a good decision for your company and steps you can take toward adopting them.
Drug development is entering a new era of potential. By applying data science techniques to real-world data we can generate evidence to complement traditional randomized clinical trials, accelerate submissions, and bring new medicines to patients.