The Rise of Real-World Evidence

    Pharmaceutical companies are increasingly interested in utilizing real-world evidence (RWE) to support decision-making and the development of clinical practice recommendations.

    RWD-based Simulations: Generating Medical Evidence from Clinical Trial and Real-World Data

    Real-world data (RWD)-based simulations use the insights derived from randomized control trials to patient populations seen in routine clinical practice and assess the impact of treatment strategies on patient outcomes.

    The case study described in the above white paper helped identify appropriate target populations for a newly launched therapy, and resultingly, impacted clinical practice guidelines. With growing recognition and appreciation of RWE studies, additional applications of RWD-based simulations can extend to market access, safety and efficacy studies, and regulatory submissions.

    Click here to download the white paper.

    Ekaterina Ponomareva

    Ekaterina Ponomareva, Ph.D.

    Senior Manager, RWE/HEOR, Axtria

    Dr. Katia Ponomareva has over 6 years of experience in advanced analytics, RWE and HEOR in the pharmaceutical domain. She strives to answer her clients' most critical questions and generate meaningful scientific evidence using medical claims, EHR patient level data and economic modeling. Recently, her focus is in causal inference analyses using observation data and use of patient level simulations to supplement clinical trial findings. She holds a Ph.D. in Economics from Lehigh University, USA.

    Alexandra Koumas

    Alexandra Koumas

    Project Leader, RWE/HEOR, Axtria

    Alexandra has over 3 years of experience in health economics outcomes research (HEOR) working with Monte Carlo patient-level simulations and mathematical modeling, primarily in the cardiovascular (CV) therapeutic area. She is well-versed in mathematical modeling and statistical techniques such as causal inference analysis, Kaplan-Meier survival analysis, manuscript writing, literature review, and protocol and statistical analysis plan development.