Systematic Approaches to Personalized Treatment Selection

Presented by Dr. Tianxi Cai, Professor of Biostatistics at School of Public Health, Harvard University

Monday, January 14th, 2013
Time: 10:30 AM – 12:00 PM Pacific / 1:30 PM – 3:00 PM Eastern
Host Location: Santa Monica, conference room 1232
Other Locations: Pittsburgh (room 6202) & Washington, DC (room 7128)

Abstract

Clinical trials that evaluate treatment benefit focus primarily on estimating the average benefit. However, a treatment reported to be effective may not be beneficial to all patients. For example, the benefit of giving chemotherapy prior to hormone therapy with Tamoxifen in the adjuvant treatment of postmenopausal women with lymph node negative breast cancer depends on the ER-status. Due to the toxicity of chemotherapy, it is crucial to identify patients who will and will not benefit from chemotherapy. This gives rise to the need of accurately predicting benefit based on important markers. In this research, we propose systematic, two-stage estimation and calibration procedures to infer about subgroup specific treatment differences to optimally future patient's disease management and treatment selections. Procedures to evaluate and compare personalized treatment assignment strategies will also be discussed. The new proposals are illustrated with the data from an AIDS clinical trial and a randomized trial for treating patients with stable coronary heart disease.

About the Presenter

Dr. Tianxi Cai is Professor of Biostatistics at School of Public Health, Harvard University. She received her Sc.D. from Harvard in 1999. Before joining the faculty of Harvard, she served as Assistant Professor of Biostatistics in University of Washington from 2000-2002. She has published more than 80 peer-reviewed papers in both methodology and collaborative fields. She is a fellow of the American Statistical Association. She has been serving on the editorial board of several academic journals, including JASA, Biometrics, Life Time Analysis, and others.

Dr. Cai's current research interests are mainly in the area of biomarker evaluation; model selection and validation; prediction methods; personalized medicine in disease diagnosis, prognosis and treatment; statistical inference with high dimensional data; and survival analysis. Dr. Cai also collaborates with the I2B2 (Informatics for Integrating Biology and the Bedside) center on developing a scalable informatics framework that will bridge clinical research data and the vast data banks arising from basic science research in order to better understand the genetic bases of complex diseases.

To Attend

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