A team of Yale scientists seeks to determine which treatment sequences produce the best results for people with advanced cancer while examining the cost of these treatments. The investigators recently received a four-year, $792,000 grant from the American Cancer Society to fund their studies.
Shi-Yi Wang, associate professor of epidemiology (chronic diseases) at Yale School of Public Health (YSPH) is the principal investigator for the grant. The co-investigators include Cary Gross, professor of medicine (general medicine) at Yale School of Medicine (YSM) and of epidemiology (chronic diseases) at YSPH, and Anne Chiang, associate professor (medical oncology) at YSM and associate cancer center director for clinical initiatives.
Cancer patients are living longer due to the availability of new therapies, but the cost of these therapies can create economic hardship for the patients and their families, the investigators said. Also, more reliable clinical evidence is needed to help guide physicians, cancer patients and their families in decision-making about future treatments, they added.
“In this project, we propose a new conceptual framework to guide prioritization and conduct of comparative effectiveness research (CER) and cost-effectiveness analyses (CEAs),” the investigators said. “Using immunotherapy for advanced non-small cell lung cancer (NSCLC) as an example, we will create a schema for prioritizing CER/CEA questions and use state-of-the-art methods to address the highest priority clinical questions in treating patients with advanced NSCLC.”
For their study, the researchers plan to conduct real-world data analysis, literature reviews, and simulation modeling to identify optimal sequential treatments for advanced NSCLC. They also will seek to identify for future research clinical scenarios with “high variation in treatment patterns and apparent evidence gaps…. We will then compare survival outcomes in the highest-priority clinical scenario.”
The team’s research could be applied to other cancers. “By integrating practice patterns from real-world data with evidence from randomized controlled trials, our proposed framework could provide a new foundation for comparative effectiveness research,” they said.