Events Calendar

Epstein Institute / ISE 651 Seminar Series

Speaker: Tianxi Cai, Professor, Department of Biostatistics, Harvard School of Public Health

Talk Title: "Evidence Based Discovery Research with Electronic Medical Records"

Series: Epstein Institute Seminar Series

Abstract: One major component of modern evidence-based medicine is the use of patients’ “baseline” information for personalized treatment selection and disease management. For instance, the benefit of giving chemotherapy prior to hormone therapy with tamoxifen for postmenopausal women with lymph node- breast cancer may vary depending on the estrogen receptor (ER) status of the tumor. ER- patients benefit substantially from chemotherapy while ER+ patients do not benefit as compared to receiving tamoxifen alone. Developing individualized decision rules for disease management can be extremely useful in practice.

To realize the goals of personalized medicine, significant efforts have been made on building risk prediction models and assessing subgroup-specific treatment effects or predictiveness of a new marker via traditional clinical trials or observational studies. In this talk, I will give a brief introduction on how one may construct virtual cohorts from the electronic medical records to conduct subsequent studies on personalized medicine. I will also discuss some of the recent statistical methods that can potentially be used to address questions arising from the field of personalized medicine.


Biography: Tianxi Cai, Professor of Biostatistics, Department of Biostatistics, Harvard School of Public Health

Education
Sc.D., 1999, Harvard University

Research
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.

In addition to her methdological research, 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.


Host: Daniel J. Epstein Department of Industrial and Systems Engineering

More Information: Seminar-Cai.doc

Location: Von Kleinsmid Center For International & Public Affairs (VKC) - Room 100

Audiences: Everyone Is Invited

Contact: Georgia Lum