Big Data has the potential to revolutionize cancer research and care, but extracting the information it holds on
the optimal strategies for cancer control will require cutting-edge tools in data science. The optimal strategies
for cancer control will be dynamic strategies that adapt clinical decisions over time to a patient’s evolving
clinical history. Unfortunately, conventional statistical methods cannot appropriately compare dynamic
strategies, so we need methods specifically designed for this task: g-methods. G-methods have helped to
shape clinical care in many areas, but they have not been systematically applied to cancer research. Further,
while g-methods let us validly estimate the effect of pre-specified strategies, these may not be the optimal
strategies. My overarching goal is to apply and further develop analytic methods to learn the optimal strategies
for cancer control from complex longitudinal data and generate user-friendly, publicly-available software to
make these methods available to the cancer research community.
I will apply these methods to answer key clinical questions across the prostate cancer control continuum: 1) the
optimal dietary and lifestyle strategies to prevent aggressive prostate cancer, 2) the optimal screening strategy
following a baseline PSA test to maximize detection of aggressive disease while minimizing detection of
indolent tumors, and 3) the optimal statin therapy strategy to maximize survival among men with nonmetastatic
prostate cancer. This project will leverage data from a large prospective cohort study and a novel platform of
electronic health records linked with genetic data. I will first apply g-methods to estimate the effects of
recommended strategies for cancer control that a randomized trial would have limited feasibility to evaluate. I
will then investigate whether novel methods that learn the optimal strategies from the data may lead to
improved, targeted recommendations that get the right interventions to the right people at the right time.
This innovative project will advance comparative effectiveness research for cancer care at the cutting edge of
data science. I am optimally positioned to undertake this research based on my 1) expertise in cancer,
epidemiology, and causal inference; 2) exceptional multidisciplinary mentoring team comprised of global
leaders in their respective fields; and 3) unparalleled research environment to support my career development.
Through this work, I will expand my expertise in new areas, including machine learning. The proposed
research and training will help me achieve my long-term career goal to become an independent investigator
and lead a transdisciplinary research program that integrates causal inference and machine learning to identify
optimal strategies for cancer control. Leveraging rich, existing data, this proposal represents a significant
opportunity to develop, apply, and disseminate powerful methods for big clinical data to accelerate progress in
cancer research and care.
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