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Steven J. Katz

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Dr. Katz’s research addresses cancer treatment communication, decision-making, and quality of care. His work aims to examine the dynamics of how precision medicine presents itself in the exam room via provider and patient communication and shared decision-making. Dr. Katz leads the Cancer Surveillance and Outcomes Research Team (CanSORT), an interdisciplinary research program centered at the University of Michigan and focused on population and intervention studies of the quality of care and outcomes of cancer detection and treatment in diverse populations.  Dr. Katz and CanSORT have been collaborating with Surveillance, Epidemiology, and End Results (SEER) cancer registries since 2002 to study breast cancer treatment decision making at the population level. We obtain patient clinical and demographic information from SEER and combine this with surveys of patients and physicians to create comprehensive data sets that enable us to study testing and treatment trends and the challenges of individualizing treatments for breast cancer patients. In 2015 we added a new dimension to our research by partnering with evaluative testing firms to obtain tumor genomic and germline genetic test results for over 30,000 breast and ovarian cancer patients in the states of California and Georgia. We are also pursuing insurance claims data to assist with our analysis of physician network effects.

Steven Katz, MD discusses BRCA and multigene sequence testing at the labs of Ambry Genetics.

Steven Katz, MD discusses BRCA and multigene sequence testing at the labs of Ambry Genetics.

Pamela Giustinelli

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I am interested in modeling, empirical, and counterfactual policy analysis of individual and multilateral decision making under uncertainty-ambiguity, especially as it applies to the family and human capital contexts. I am also interested in survey methodology, particularly as it relates to this line of research. Here are some important questions in my research agenda:

  • How do preferences, beliefs, choice sets, and other elements of a choice situation determine what choices people make and also how they make those choices? (That is, the “decision rules,” “decision protocols,” or “modes of interactions” they use.) And how are those elements formed?
  • What information do individuals and groups have or use when making decisions under uncertainty? And what information is or is not shared among decision makers in multilateral settings?
  • What are the implications of the above points for policy?
  • To inform modeling, identification, and prediction of choice behaviors, what components of individuals’ and groups’ decision processes can we sensibly measure in surveys? From whom? And in what formats?

Data science methodology: Survey design for elicitation of components of human decision processes and interactions under uncertainty/ambiguity

Data science applications: Human capital (school choice, labor supply, end-of-life living arrangements)
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