Alexia Iasonos is focused on the design and analysis of clinical trials, primarily dose-finding phase I trials that are model based, as well as predictive modeling in surgical outcomes. Her methodological research is focused on evaluating Bayesian adaptive phase I designs, specifically the Continual Reassessment Method (CRM). She has compared CRM with the standard 3+3 dose-escalation scheme [Abstract] as part of a two-year award from Memorial Sloan-Kettering’s Experimental Therapeutic Center to study dose-finding algorithms. She has lead a team of programmers and data managers in order to create the infrastructure (software and a web interface) to facilitate clinical personnel in obtaining the dose allocation seamlessly, so that CRM can be implemented in future Memorial Sloan-Kettering trials.
Her clinical collaborations focus on ovarian cancer. Her work with the Gynecological Oncology Group varies from exploring biomarkers to predicting survival outcomes. Most recently she has been involved in projects including:
Alexia has previously worked with QoL outcomes as part of collaboration with Health Outcomes (HOR) in which the group analyzed patient-reported outcomes and compared these with clinician symptom reporting using the National Cancer Institute common terminology criteria for adverse events. She has also worked with the urology group in developing predictive models and nomograms that can be used preoperatively to enable a more informed clinical decision-making process.
She continues to serve as the statistical reviewer for the Phase I-II Data and Safety Monitoring Committee and the primary statistician for the Developmental Therapeutic Center and Quality Control Initiative at Memorial Sloan-Kettering. She is currently serving as a scientific reviewer on the ASCO program committee and on Susan G. Komen for the Cure Foundation.