||1R35CA274442-01 Interpret this number
||Fred Hutchinson Cancer Center
||Modeling and Analytics for Cancer Diagnostics: Traversing the Data-Evidence Divide
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 successful execution of the Research Program will improve our understanding of how
novel cancer diagnostics impact clinical and policy relevant outcomes so that these technologies can be used
wisely and equitably to improve care for all cancer patients.
Short-term Endpoints for Cancer Screening Trials: Does Tumor Subtype Matter?
, Gogebakan K.C.
, Menon U.
, Gulati R.
, Weiss N.S.
, Etzioni R.
Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology, 2023-06-01; 32(6), p. 741-743.
Shopping for New Cancer Screening Tests.
, Castle P.E.
Journal of clinical oncology : official journal of the American Society of Clinical Oncology, 2023-05-10; 41(14), p. 2471-2473.