Real-world evidence (RWE), also referred to as “big data”, is an increasingly hot topic in reimbursement, especially for oncology therapies where exciting new therapies may be approved based on a limited evidence base (e.g. Phase I/Phase II trial data, small sample sizes (precision medicines) or shorter trial durations with surrogate efficacy measures (progression-free survival rather than overall survival). Given the potential for RWE in supporting reimbursement decisions, this topic has been the focus of many industry symposia and research initiatives in recent years.

In Canada, oncology data are captured by provincial health systems via cancer registries, for instance, the British Columbia Cancer Registry, the Alberta Cancer Registry, and Cancer Care Ontario. These registries collect important information relevant to oncology studies, including information on diagnosis, staging, treatment and mortality.  Research opportunities are further enhanced by the ability to link databases using unique patient identifiers; this allows researchers to compile information from a provincial cancer registry, with other provincial datasets, including hospitalizations, ambulatory care and outpatient visits, as well as pharmaceutical information and lab data. The ability to link these complex and large databases affords researchers the unique opportunity to create a robust research database, allowing payers, providers and industry to better understand patient profiles, treatment patterns, and clinical outcomes in real-world settings.

To further increase the capacity for RWE studies, the Pan-Canadian Real-world Health Data Network was created to create a research data infrastructure to support multi-provincial studies, such as creating algorithms that would allow cross-provincial linkage of physician claims data. [1] It is possible that such research infrastructure may eventfully facilitate cross-jurisdictional linkage of oncology data that can be used to guide reimbursement decisions across several of the Canadian public payer systems.

As an independent and qualified Canadian research group, Medlior can access and analyse administrative patient data from provincial health systems. Medlior’s team of experienced clinical consultants and biostatisticians can help you navigate the available Canadian data sources to design and execute customized research projects to answer your RWE questions.

Let Medlior help you bridge the efficacy-effectiveness gap, through projects like:

• Using RWE to connect a disconnected network of evidence
• Modelling to predict long-term effectiveness
• Adjusting for bias in non-randomised and observational studies
• Reweighting clinical trial data to mimic real-world populations

For more information and to contact Medlior visit Real World Evidence

[1] www.prhdn.ca