Real-World Data-Based Simulations: Generating Medical Evidence From Clinical Trial And Real-World Data

Randomized clinical trials are the gold standard of evidence to support decision-making and the development of clinical practice recommendations. Yet, by design, they typically assess a drug’s safety and efficacy on a narrow patient population with pre-defined treatment strategies and over a short time horizon. Real-world data (RWD)-based simulations allow us to extend the insights already derived from RCTs to patient populations seen in routine clinical practice and assess the impact of treatment strategies on patient outcomes over a longer follow up.

Using information on the real-world population, such as demographics and clinical conditions, together with estimated baseline risk of the population and effect of treatment, RWD-based simulations can demonstrate the benefit of proposed treatment interventions. With the ability to model the influence of real-world circumstances such as adherence, persistence, and payer restrictions, evidence provided by RWD-based simulations can be used to inform physician decision-making in clinical practice, therapeutic area guidelines, regulatory submissions, and more.

In this white paper, we discuss in detail the overall framework, advantages, and applications of such models. We also present a case study of a highly flexible RWD-based simulation of cardiovascular (CV) therapy intensification developed by Axtria’s Real-World Evidence/Health Economics Outcomes Research team, which was cited in the 2018 American College of Cardiology/American Heart Association clinical practice guidelines on the treatment of high-risk atherosclerotic cardiovascular disease (ASCVD) patients.