Session Chair Profile
M.D., Professor, Medicine, Harvard Medical School; Chief, Division of Population Sciences, Medical Oncology, Dana-Farber Cancer Institute
Biography
Dr. Schrag received her medical degree from Columbia University in New York in 1991. She subsequently completed her residency in Internal Medicine at Brigham and Women’s Hospital, and her fellowship in Medical Oncology at Dana-Farber Cancer Institute. She obtained a Masters in Public Health from the Harvard School of Public Health in 1998, and joined the staff of DFCI and Brigham and Women’s Hospital. From 1999 through 2007, Dr. Schrag practiced medical oncology in the Division of Gastrointestinal Oncology at Memorial Sloan-Kettering Cancer Center, where she was an Associate Member and Associate Professor of Public Health and Medicine. In 2007, she returned to DFCI and Brigham and Womens Hospital, where she is a medical oncologist and clinical investigator in the Center for Gastrointestinal Oncology. Her research focuses on utilization of new cancer treatment technologies at the population level.
Session Abstract – PMWC 2019 Silicon Valley
Session Synopsis: Traditionally, the majority of evidence regarding the benefits and risks of cancer treatments is derived from clinical trial populations. However, the vast majority of cancer patients receive treatment outside the context of clinical trials. There is tremendous motivation to evaluate the benefits and risks of cancer treatments delivered in the context of “real world” care. Real-world evidence (RWE) is typically defined as treatment that is not delivered in accordance with an investigational protocol and therefore lacks clearly specified endpoints and assessment intervals for determining benefit. This session will cover opportunities and challenges in using real-world evidence to inform clinical decision making in cancer. The session will review opportunities and challenges in defining real world endpoints and the development of methods and analytical tools capable of generating insights from RWD.
