Study Aims To Resolve How To Manage Pre-Cancers Of The Breast

The first large U.S. study aimed at resolving an ongoing debate about the best way to treat an early sign of breast cancer will launch later this year under the direction of a Duke Cancer Institute investigator. The study, entitled COMET (Comparison of Operative to Medical Endocrine Therapy) for low-risk ductal carcinoma in situ, received funding through a $13.4 million, five-year award from the Patient-Centered Outcomes Research Institute (PCORI), an independent, nonprofit organization authorized by Congress in 2010 to support research that enlightens health care decisions.  

Principal investigator E. Shelley Hwang, M.D., chief of breast surgery at the Duke Cancer Institute and vice chair of research in the Duke University Department of Surgery, will lead the study through the cooperative group, The Alliance for Clinical Trials in Oncology.   The study was designed in collaboration with Terry Hyslop, PhD, and Director of Biostatistics for the Duke Cancer Institute.  Dr. Hyslop will direct the biostatistical aspects of the study.  The design of the study was particularly challenging in that, it is anticipated that patients may not want to remain in their randomized assignment arm, particularly if assigned to active surveillance.  The notion of active surveillance, which focuses on trying to minimize over-treatment of patients, may be hard for some patients to accept.  Thus, intention-to-treat may not be a viable option due to bias from non-compliance.  Additional challenges analytically include the development of an observational registry study that will incorporate all eligible patients across the Alliance and other centers whether they were approached to participate in the trial or not.  This pragmatic component adds to the richness of the data, providing for collection of long-term outcomes, yet generates a mixture or randomized and observational data.  The study team plans to generate simulated data throughout the trial, using actual patient compliance data, to determine the best analytic approach for causal inference in this setting. Click here to read more.

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