Analytical tools and methods: individualized and innovative
Various sources feed a huge, valuable stock of real-world data (RWD). There is a great desire to make this data fully usable for research, as RWD provides compelling evidence that supports innovation, regulatory requirements, and industry commitments.
Extracting the most relevant information from RWD and uncovering new correlations require innovative solutions that push beyond conventional methods. Evin Hub designs and implements new methods and technologies tailored specifically to meet your unique research needs..
Secondary data - such as billing data, clinical registry data, digital health data or even synthetic data - provides a valuable resource for comprehensive and cost-effective analyses. Machine learning methods allow this data to be processed quickly and accurately, combined, patterns identified and predictive models developed.. By combining these technologies, we can gain deeper insights into disease patterns and treatment effects, promote personalized medicine, and improve the efficiency of healthcare overall.“
Dr. Ines Weinhold, Head of Science Management and Communication
The development of analytical tools is a flexible, application-oriented process carried out in close consultation with you. Our work is empirically based from the initial knowledge gap analysis and method design to the validated medical algorithm. We use a full repertoire of descriptive statistics, complex AI-based analyses, and synthetic data. Using efficient, privacy-compliant algorithms and data structures, we can accurately select, process, and analyze large volumes of data from multiple sources.
Services at a glance
- development of risk scores and predictive models
- model adjustments and validation based on various data sources
- transfer of variables and results from different study populations or cohorts through statistical matching and algorithm engineering
- development and integration of individualized and modular stand-alone analysis tools such as the Evin Hub Profiler and the generation of synthetic data