Developing sound strategies for prostate cancer control requires integrating information from a vast collection
of clinical trials and observational studies and developing statistical and computer models to fill in inevitable
evidence gaps. Management of prostate cancer is particularly controversial because of the absence of a
consensus about optimal screening and treatment approaches. The PI of this application is a member of a
team that develops statistical methods and computer models to advance sound prostate cancer policies. This
work can only be done by a skilled specialist in cancer surveillance and statistical modeling. It demands
expertise in complex statistical computing, which must be meticulously executed and validated, internally and
externally. It requires outstanding writing skills to bring projects from the inquiry phase through the publication
process, and it requires a proven ability to communicate and collaborate across multidisciplinary teams. The PI
of this application has used these skills and others to establish his research group as a leader in prostate
cancer modeling and policy. In this application he proposes to use these skills in support of new modeling
analyses including (1) targeted screening in high-risk patient subgroups, (2) frameworks for decision making
around novel biomarkers for screening and treatment, and (3) advancing methods to estimate overdiagnosis—
the detection by screening of cancers that would never be diagnosed otherwise. Additionally, this application
will support (1) developing online calculators and decision aids to assist clinicians and policymakers in
selecting appropriate recommendations and (2) providing high-quality biostatistical service in a multi-project
translational prostate cancer research program. The overarching aim is to optimize interventions to reduce
morbidity and mortality from this most common cancer in men.
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