The use of Real-World Data (RWD) in health economics research has been significantly increasing over the past decade. The growing availability of longitudinal observational health data together with big-data analytics has now enabled us to go beyond the efficacy of interventions and inform health policies. Concurrently, policy decision-makers have established guidelines to incorporate Real-world Evidence (RWE) as a required component into the approval process for new therapeutics. This includes CADTH and Health Canada’s recent joint initiative to optimize the use of RWE for regulatory decisions in order to improve the extent of access to prescription drugs in Canada.   

The rise in availability of RWD has been supplemented with the improvement in computational methods for disease simulation models ranging from cohort-based, i.e., Markov models and system dynamics, to patient-level models, i.e., discrete-event and agent-based. While computer models have been vastly used in HTA, the role of computational methods in RWE, however, has not been fully acknowledged.  

One application of RWE is in populating disease simulation models, i.e., to extract epidemiological characteristics (incidence rates across categories of risk factors), resource use (GP visit, hospitalization, prescription drugs) and disease-stage (risk classification, patient pathway). Together with validated demographic projections, such models can project health and cost determinants of a heterogeneous population over time. These models augment data from other sources for measures not captured in RWD, e.g., patient-reported outcomes for QoL, productivity loss, and clinical characteristics, e.g., patient charts, clinic’s EMR.  

Another application of modelling in RWE is to extend the scope of traditional Budget Impact Models (BIM) and assess the societal health and economic influence of real-world policies before implementation. Policy decision-makers and public payers are often interested in evaluating “counterfactuals” or what-if scenarios that cannot be answered only by epidemiological studies. For instance, modelling can be used to answer how change in treatment guidelines for Multiple Myeloma (MM) cancer patients would cost the payer in the next 10 years or how value-based pricing regimen for a CAR-T drug may affect the long-term spending of a public payer.  

Economic Modeling at MEDLIOR 

MEDLIOR has a team of health economists and modelling experts with expertise in the development of simulation models using RWD. To meet payer and regulatory evidence requirements, our team of researchers offers leadership in the design and execution of modelling studies, supported by our multidisciplinary team in evidence generation and RWD analytics. We have access to national and provincial population data to support demographic projection models together with state-of-the-art patient-level and Markov structure models. Furthermore, we are eligible to access and link Health Systems Data sources in Alberta that capture health and economic measures for the Albertan population (4.2 million) for the past 20 years. A more recent and exciting development is our eligibility to access the soon-to-be-released linked dataset from Statistics Canada including Canadian Income Data, Canadian Cancer Registry and Health System Data sources for national and provincial-level analyses. 

For more information and to find out how Medlior can help your project contact us today!