Continuous Innovation in Cancer Research
Initially, RLA’s role consisted of conducting an analysis of the public policy environment and identifying barriers to rapid research and development in oncology. Development of the CII soon followed—the first, evidence-based, customizable online tool to review progress against cancer over time. The CII provides an interactive platform for conducting a variety of analyses across 12 types of cancer. RLA annually updates the CII (through 2020) with the latest published clinical data. Watch a White Board Video or Download Brochure of the CII. From this collaboration, RLA has also produced noteworthy publications. For example, one paper measured the value of surrogate endpoints in oncology dynamic assessments (2017), and another investigated the utility of real-world evidence in personalized medicine using homomorphic encryption. This proof-of-concept paper was selected by the International Medical Informatics Association (IMIA) as one of the three best papers in clinical research informatics published in 2019.
RLA continues to explore opportunities to build on its past efforts in evidence-based oncology research that benefits patients and fosters innovative industry collaboration. We continue to harness the CII’s unique utility of published clinical data to identify long-term trends, impactful advances, and unmet patient needs, thus informing audiences of the novel advancements in oncology research. This experience has positioned RLA as a leader in oncology health policy analysis with the ability to assess international oncology frameworks and leverage the CII’s potential into novel areas such as program evaluation.
Work products and links
PACE Continuous Innovation Indicators—a novel tool to measure progress in cancer treatments (eCancerMedicalScience, 2015)
Dynamic value assessments in oncology supported by the PACE Continuous Innovation Indicators (Future Oncology, 2017)
The PACE continuous innovation Indicators: A flexible tool to evaluate progress in cancer treatments (Journal of Clinical Oncology, 2017)
Proof-of-concept study: Homomorphically encrypted data can support real-time learning in personalized cancer medicine (BMC Medical Informatics and Decision Making, 2019)