Mousumi Banerjee, Ph.D., M.S.
Dr. Banerjee is a biostatistician whose applied research focuses on using data from population-based cancer registries to investigate disparities in patterns of cancer care and outcomes. The primary objective of her prognostic modeling work is to develop tools that can be taken into the clinic setting and used to help make decisions about the most appropriate and effective treatments for patients.
- Ph.D., Statistics, University of Wisconsin
- M.S., Statistics, Indian Statistical Institute
- B.S., Statistics, Indian Statistical Institute
Health Services Research & Policy Focus
What are you thinking about?
My background is rooted in statistical modeling with a primary focus on studying cancer. Early in my career I focused on two kinds of work: (1) prognostic modeling in cancer, that is, developing mathematical models related to patient outcomes that can help in making treatment decisions, and (2) performing studies using cancer registry data through the lenses of epidemiology, health outcomes, and healthcare delivery. Currently I am involved in many different types of work across various types of cancer as well as a variety of other medical conditions. I am part of a thyroid cancer study group that focuses on topics related to diagnosis, prognosis, management, and survivorship; specifically looking at reasons and implications for the rise in incidence of thyroid cancer, quality of care, variation in management, patient physician communication, and thyroid cancer outcomes. I am also studying poor prognosis cancers, and how treatment, spending, and associated costs are distributed for cancer and non-cancer end-of-life care. These studies also look into the variation of end-of-life care across ethnicities.
Why is this interesting to you?
Data speaks to me. I am fascinated by data and using statistical methods to uncover the stories that data can tell. Although my academic training was heavily theoretical, I have become solidly invested in the science behind the critical issues that our world is facing, specifically in the areas of health and healthcare. Data can be used to develop real solutions to these problems. I tell my graduate students to take the time to understand the scientific question first before approaching the statistical solution, because otherwise they might end up giving the right answer to a wrong question.
What are the practical implications for healthcare?
The overarching theme of my work is to study variations – in patterns of care and patient outcomes. The primary objective of my prognostic modeling work is to develop tools that can be taken into the clinic setting and used to help make prudent decisions about the most appropriate and effective treatments for patients. It is through my work at IHPI and CHOP that my findings can be translated and implemented into the real world and make a significant difference. I love the collaboration that underlies every part of my work.