Optimize your trial design for clinical and commercial success

To maximize the value of your molecule, you need an efficient, systematic approach that brings real-world data and patient perspectives into your clinical trial design process. You also need to understand which patients might respond best to your treatment to avoid trial failures and minimize adverse effect incidents. With a better informed protocol and enriched patient sub-populations, clinical trials can be executed with greater confidence and predictability, with shorter timelines and richer evidence of value to stakeholders. 

Sub-population Optimization Solutions (SOS) uses AI and Machine Learning to identify patients with best treatment response and least side effects. This knowledge together with information from a wide range of sources – including electronic medical records, clinical research data, public data sources, and industry benchmarking data – gets analyzed and then translated it into actionable insights, enabling informed strategic decisions. 

IQVIA’s data-driven study design approach delivers trial efficiency and predictability. 

  • Reduces risk of trial failure and adverse effect incidents.
  • Optimizes trial size and reduces time to market for drugs.
  • Substantially reduces protocol amendments due to planning, feasibility and enrollment. 
  • Uses breakthrough technology to identify the right data sets, test and model assumptions, examine pros and cons to make the right decisions. 
  • Enhances expertise through a collaborative process that brings your experts together with ours to optimize your design and get predictable results. 

If you have already developed your study protocol, IQVIA’s Protocol Validation checks your protocol against real-world data before your study starts – for precision, clarity and consistency. 

Let us help you optimize your clinical trial design for quality and performance.

Affiliated solution

Protocol Validation
Reduce your risk and increase predictability by validating your protocols against real-world data.