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.
More information on Tianxi Cai may be found at http://www.hsph.harvard.edu/tianxi-cai/
This Colloquium is sponsored jointly by the University of Georgia Department of Statistics and the University of Georgia Department of Epidemiology and Biostatistics.