Outcomes & Effectiveness

Novel therapies have improved the survival of patients with cancer but have also increased treatment costs substantially. These therapies create an enormous financial burden for patients, their families, and society in general. Furthermore, there are several critical barriers to generating reliable evidence to guide treatment decision-making. First, there is a lack of head-to-head comparative evidence. Also, as the cancers progress, patients’ regimens can involve multiple lines of treatment. Thus, the relevant research question for clinical evidence often becomes not simply how one drug compares to another, but which treatment sequence may produce the best patient outcomes. This project will combine literature reviews, secondary data analyses, and simulation modeling to identify the optimal sequential treatments for advanced non-small cell lung cancer. 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.

Funding source: American Cancer Society

Principal Investigator: Shi-Yi Wang

This study aims to understand the measurement characteristics and relationships among various physical function assessments and activity data collected from a wearable device in cancer patients undergoing chemotherapy. The goals of the research are to: (1) collect data on physical function using clinician- and patient-reported methods, performance measurement, and wearable devices; (2) compare the challenges associated with collecting information using each physical function assessment tool; (3) compare longitudinal changes in physical function with the likelihood and magnitude of side effects from chemotherapy; and (4) conduct a structured exit interview to evaluate burden and usability across the different physical function evaluation modalities and a wearable device. The ultimate goal of this project is to provide evidence and knowledge to guide the selection of PF assessment modality/tool(s) to be incorporated in cancer clinical trials as an endpoint for regulatory and treatment decisions.

Funding source: U.S. Food and Drug Administration

Principal Investigator: Cary Gross

This project uses AI models applied to unstructured electronic health record text to predict patient-reported quality-of-life outcomes in patients with prostate cancer, with the goal of translating routinely collected clinical information into actionable insights about symptom burden, disease trajectory, and patient experience.

Funding Source: Yale Cancer Center
Principal Investigator: Michael Leapman