Gina L. Mazza, Ph.D., strives to improve the design, conduct and analysis of cancer clinical trials through her collaborations and research. Her research focuses on the development and advancement of methods for addressing missing data and evaluating patient-reported outcomes. By promoting the use of optimal methods for addressing missing data, Dr. Mazza ensures more valid and efficient testing of the safety and effectiveness of new treatments. Her research also advances the ability to incorporate the patient's voice in the testing process.
- Cancer clinical trials. Dr. Mazza collaborates with multidisciplinary teams on the design and analysis of cancer clinical trials through her involvement in the Alliance for Clinical Trials in Oncology and the National Cancer Institute's Community Oncology Research Program.
- Missing data. Dr. Mazza develops and evaluates methods for addressing missing data. In cancer clinical trials, optimal use of the available data supports more valid and efficient testing of the safety and effectiveness of new treatments.
- Patient-reported outcomes. Dr. Mazza collaborates with multidisciplinary teams on the development of international standards for incorporating patient-reported outcomes in cancer clinical trials. Patient-reported outcomes enhance clinicians' and researchers' understanding of patients' experiences before, during and after treatment, particularly when these experiences are difficult or impossible to observe.
Significance to patient care
The improvement of patient care inspires Dr. Mazza's research. By advancing the scientific rigor of cancer clinical trials, Dr. Mazza supports safer, more effective treatments and informs shared treatment decision-making between clinicians and patients. She promotes direct reporting by patients regarding their perceived health; presence, frequency or severity of symptoms; health-related quality of life; and treatment satisfaction. Relative to clinician reports, patient reports demonstrate greater sensitivity to changes in daily functioning or symptom burden, thus allowing for improvements in safety monitoring, symptom management and even survival. Furthermore, properly accounting for missing data supports more accurate and generalizable causal inferences regarding the safety and effectiveness of treatments in cancer clinical trials.