This Project Grant award from the National Cancer Institute (CFDA 93.394 - Cancer Detection and Diagnosis Research) to Yale University totals $167,500 and will be executed from September 2023 through August 2025. The primary objectives are to: Develop deep neural network-based emulation analysis methods and software to objectively quantify the relative effectiveness of drugs, devices, and treatment procedures on cancer prognosis, especially when randomized clinical trials are not feasible. The methodological advancements will expand the scope of emulation analysis, deep learning, causal inference, and observational data analysis. Conduct emulated clinical trials to analyze the comparative effectiveness of (A) lobectomy versus limited resection for lung cancer survival in the SEER-Medicare elderly population, and (B) radical prostatectomy versus observation for localized prostate cancer survival in the VA population. The findings from these analyses will directly inform clinical practice. The project will leverage large observational datasets from electronic medical records and insurance claims to complement randomized trials, and deliver user-friendly software tools to enable broader adoption of these advanced analytical techniques. This work has the potential to lead to more definitive findings on cancer treatment effectiveness.
Mod # | Description | Reason For Modification | Federal Obligation (Click to sort descending) | Date (Click to sort ascending) |
|---|---|---|---|---|
| Not listed | $167.5k | 9/19/23 |