Europe, Middle East & Africa
Uncover sub-populations in your trial data.
Identifying sub-populations can be the key to meeting a trial's primary endpoints. IQVIA helps you find those patients, preserve your investment, and ensure the right therapies reach the right patients.
A new approach to trial rescue
If your trial fails to meet its Phase II or Phase III endpoints, you can be faced with difficult choices. Either abandoning or deprioritizing the product. Both of these options have significant consequences for your business and the patients you strive to help.
Today, advanced analytics such as machine learning are enabling new, more precise views into the data, identifying predictive biomarkers and generating sub-groups based on characteristics that might yield different results.
Capturing this data can be the difference between a successful trial and a discarded product.
Hidden opportunities in sub-groups
IQVIA's Sub-Population Optimization solution identfies sub-populations from your trial data where the treatment effect is more pronounced, avoiding both bias an statistical type-1 errors. This can support
- Patient-centric proof of efficacy
- Differentiation between similar therapies, earning regulatory and payer approval
- Improvement in patient inclusion/exclusion criteria
How it works
A customized solution built around your molecule and patient population
Precision Medicine powered by the IQVIA CORE
The power of the IQVIA CORE integrates your data with IQVIA's advanced analytics, technology and domain expertise to develop purpose-built algorithms that identify patterns with greater precision.
Powered by the CORE, IQVIA's advanced statisticians can suggest enhanced study designs or new development strategies by finding patterns and connections in trial results.
Our team also ensures that data and parameters are consistent with industry standards and best practice, so you can decide with confidence how to move forward.
Finding Value in Failing Trials
How AI-driven platforms can save failing trials by using data to hone protocols, identify sub-populations with shared traits, and ensure the right patients are recruited to demonstrate success
Identify the best performing sites for your trial in less time with new machine learning and predictive modeling methods.
Predict enrollment more accurately and increase the number of patients per site using data-driven patient recruitment solutions.