Current Research Interests:
Dr. Alexia Iasonos is primarily focused in the design and analysis of clinical trials, predictive modelling, and quality of life (QoL) analysis. Her methodological research is focused on evaluating Bayesian adaptive Phase I designs, specifically the Continual Reassessment Method (CRM). In her recent work, she compared CRM with the standard 3+3 dose escalation scheme. [Abstract] She has been awarded a 2-year grant from the Experimental Therapeutic Center of MSKCC to study this further. She has been leading 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 MSKCC 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:
- Evaluating clinical endpoints in ovarian patients in second remission or greater: this analysis includes data from four Phase II trials or vaccines given for consolidation or maintenance therapy.
- Assessing CA-125 levels as a predictor of progression free survival and overall survival in ovarian cancer patients with surgically defined disease status at the end of primary therapy.
- Collaborations with the Pathology Service looking at various immunohistochemical markers in epithelial and ovarian cancer and assessing relationships to histology, distant metastases, and outcome.
Alexia is particularly interested in the statistical analysis of QoL outcomes. In her most recent collaboration with Health Outcomes (HOR), the group analyzed lung and prostate patient versus clinician symptom reporting using the National Cancer Institute common terminology criteria for adverse events. She is also actively involved in assessing online patient reported outcomes.
Alexia previously worked with the Urology service concentrating on surgical and oncological treatment. She was mainly involved in developing predictive models and nomograms that can be used pre-operatively to enable a more informed clinical decision-making process. Specifically, she has utilized pre-operative ultrasonography to predict the histology of renal lesions.
She continues to serve as the statistical reviewer for the Phase I-II Data and Safety Monitoring Committee and the Phase I Working Group at MSKCC. She represents Biostatistics/ HOR/ Epidemiology service at the Junior Faculty Council.
Selected Bibliography:
- Iasonos A., Wilton A., Riedel E., Seshan V., Spriggs D. A comprehensive comparison of the Continual Reassessment Method to the standard 3+3 dose escalation scheme in Phase I dose-finding studies. Clin Trials. 2008;5(5):465-77.[PubMed Abstract]
- Iasonos A, Schrag D, Raj GV, Panageas KS.How to build and interpret a nomogram for cancer prognosis. J Clin Oncol. 2008 Mar 10;26(8):1364-70. Review. [PubMed Abstract]
- Abu-Rustum NR, Iasonos A, Zhou Q, Oke E, Soslow RA, Alektiar KM, Chi DS, Barakat RR. Is there a therapeutic impact to regional lymphadenectomy in the surgical treatment of endometrial carcinoma? Am J Obstet Gynecol. 2008 Apr;198(4):457.e1-5; discussion 457.e5-6. [PubMed Abstract]
- Basch E, Iasonos A, Barz A, Culkin A, Kris MG, Artz D, Fearn P, Speakman J, Farquhar R, Scher HI, McCabe M, Schrag D. Long-term toxicity monitoring via electronic patient-reported outcomes in patients receiving chemotherapy. J Clin Oncol. 2007 Dec 1;25(34):5374-80. [PubMed Abstract]
- Basch E, Iasonos A, McDonough T, Barz A, Culkin A, Kris MG, Scher HI, Schrag D. Patient versus clinician symptom reporting using the National Cancer Institute Common Terminology Criteria for Adverse Events: results of a questionnaire-based study. Lancet Oncol. 2006 Nov;7(11):903-9. [PubMed Abstract]
- Raj GV, Bach AM, Iasonos A, Korets R, Blitstein J, Hann L, Russo P. Predicting the histology of renal masses using preoperative Doppler ultrasonography. J Urol. 2007 Jan;177(1):53-8. [PubMed Abstract]
