The field of cancer diagnostics is in a rapidly expanding growth phase that goes hand in glove with the precision medicine revolution. However, the rapid pace at which new technologies are entering the marketplace makes rigorous evaluation via controlled studies infeasible for all but a relative few. This means that while we typically have some data about diagnostic test performance, we frequently lack evidence regarding the outcomes that drive clinical and policy decisions. The Research Program outlined in this application will tackle this data- evidence divide using the tools of modeling and analytics. Modeling is an increasingly accepted discipline for integrating knowledge about the process by which diagnostic performance drives outcomes. Analytics is the use of statistical learning techniques to fill in the knowledge gaps and to propagate uncertainty from model inputs to outcomes. The Principal Investigator has built a leading research program in modeling and analytics for evidence generation in cancer policy. Among many methodologic and substantive contributions, her work has informed prostate cancer screening guidelines from national policy panels, established best practices for estimation of overdiagnosis, and produced specific directions for screening high-risk populations including Black men. The Research Program outlined in this application will harness the modeling and analytics skillset developed by the Principal Investigator over nearly three decades to build a framework and tools for evidence generation around cancer diagnostics. The application details a sequence of projects for two technologies that are generating intense current interest with wide-ranging practice implications and serious evidence gaps: Multi-cancer early detection testing, and PSMA-PET/CT for newly diagnosed and recurrent prostate cancer. The MCED work will deepen our understanding of performance characteristics, provide guidance regarding a defensible test confirmation strategy, project benefits and harms of different MCED strategies and offer new ideas for shortcutting the typically lengthy process of cancer screening trials. The PSMA-PET/CT work will develop an approach for updating treatment benefit estimated derived from trials that included a mixture of patients with unknown PSMA status and will project lives saved of treatment reallocation on the basis of PSMA-PET.CT result. The tools and processes developed for modeling these technologies will be applicable to other new diagnostics that emerge during the lifetime of the Research Program. The modeling work will be accompanied by a sequence of real-world analytics projects to assess dissemination of and disparities in uptake of novel diagnostics and their consequences for healthcare utilization and costs. This work will establish collaborations with new real-world data partners and materially expand the Principal Investigator’s skillset to encompass a greater competency in medical informatics. The success...